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86 Commits

Author SHA1 Message Date
Xuwznln
f6b2bfaf8e upgrade to 0.11.1 2026-04-22 19:54:59 +08:00
Xuwznln
71107e9552 use gitee to install pylabrobot
fix virtual import
2026-04-22 19:54:57 +08:00
Xuwznln
1ad4766221 fix possible conversion error 2026-04-22 19:54:55 +08:00
Xuwznln
67a74172dc v0.11.0 2026-04-16 01:35:01 +08:00
Xuwznln
ccbf5378dd update workbench example
update aksk desc

print res query logs

Fix skills exec error with action type

Update Skills

Update Skills addr

Change uni-lab. to leap-lab.
Support unit in pylabrobot

Support async func.

change to leap-lab backend. Support feedback interval. Reduce cocurrent lags.

fix create_resource_with_slot

update unilabos_formulation & batch-submit-exp

scale multi exec thread up to 48

update handle creation api

fit cocurrent gap

add running status debounce

allow non @topic_config support

update skill

add placeholder keys

always free

提交实验技能

disable samples

correct sample demo ret value

新增试剂reagent

update registry

新增manual_confirm

add workstation creation skill

add virtual_sample_demo 样品追踪测试设备

add external devices param
fix registry upload missing type

fast registry load

minor fix on skill & registry

stripe ros2 schema desc
add create-device-skill

new registry system backwards to yaml

remove not exist resource

new registry sys
exp. support with add device

correct raise create resource error

ret info fix revert

ret info fix

fix prcxi check

add create_resource schema

re signal host ready event

add websocket connection timeout and improve reconnection logic

add open_timeout parameter to websocket connection
add TimeoutError and InvalidStatus exception handling
implement exponential backoff for reconnection attempts
simplify reconnection logic flow
2026-04-16 01:33:36 +08:00
Xuwznln
c001f6a151 v0.10.19
fast registry load

minor fix on skill & registry

stripe ros2 schema desc
add create-device-skill

new registry system backwards to yaml

remove not exist resource

new registry sys
exp. support with add device

add ai conventions

correct raise create resource error

ret info fix revert

ret info fix

fix prcxi check

add create_resource schema

re signal host ready event

add websocket connection timeout and improve reconnection logic

add open_timeout parameter to websocket connection
add TimeoutError and InvalidStatus exception handling
implement exponential backoff for reconnection attempts
simplify reconnection logic flow

add gzip

change pose extra to any

add isFlapY
2026-03-22 04:25:07 +08:00
Xuwznln
145fcaae65 support container as example
add z index
2026-03-03 18:03:53 +08:00
Xuwznln
a79c0a88bf fix container volume
update materials

更新prcxi deck & 新增 unilabos_resource_slot

new workflow & prcxi slot removal

fix size change
2026-03-03 17:20:06 +08:00
Xuwznln
06b6f0d804 v0.10.18 2026-02-28 09:53:31 +08:00
Xuwznln
b551e69f64 no opcua installation on macos
fix possible crash

fix deck & host_node

set liquid with tube

add test_resource_schema

fix test resource schema

registry update & workflow update

add test mode

support description & tags upload

fix config load

fix log

add registry name & add always free

correct config organic synthesis

Adapt to new scheduler, sampels, and edge upload format (#230)

* add sample_material

* adapt to new samples sys

* fix pump transfer. fix resource update when protocol & ros callback

* Adapt to new scheduler.

Feat/samples (#229)

* add sample_material

* adapt to new samples sys

adapt to new samples sys

adapt to new edge format

workflow upload & prcxi transfer liquid

lh liquid

speed up registry load

workflow upload & set liquid fix & add set liquid with plate

fix upload workflow json
2026-02-28 09:46:46 +08:00
Xuwznln
5179a7e48e workflow upload & set liquid fix & add set liquid with plate
fix upload workflow json

save class name when deserialize & protocol execute test

Support root node change pos

add unilabos_class

gather query
2026-02-02 18:26:41 +08:00
Xuwznln
3a2d9e9603 transfer liquid handles
add msg goal

Fix OT2 & ReAdd Virtual Devices
2026-01-28 11:46:54 +08:00
Xuwznln
a277bd2bed CI Check use production mode 2026-01-27 19:59:22 +08:00
Xuwznln
176de521b4 v0.10.17 2026-01-27 19:41:12 +08:00
dependabot[bot]
38c5c267af Fix Conda Build
ci(deps): bump actions/checkout from 4 to 6 (#223)

Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 6.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v4...v6)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

ci(deps): bump actions/upload-pages-artifact from 3 to 4 (#225)

Bumps [actions/upload-pages-artifact](https://github.com/actions/upload-pages-artifact) from 3 to 4.
- [Release notes](https://github.com/actions/upload-pages-artifact/releases)
- [Commits](https://github.com/actions/upload-pages-artifact/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/upload-pages-artifact
  dependency-version: '4'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

ci(deps): bump actions/upload-artifact from 4 to 6 (#224)

Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 6.
- [Release notes](https://github.com/actions/upload-artifact/releases)
- [Commits](https://github.com/actions/upload-artifact/compare/v4...v6)

---
updated-dependencies:
- dependency-name: actions/upload-artifact
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

ci(deps): bump actions/configure-pages from 4 to 5 (#222)

Bumps [actions/configure-pages](https://github.com/actions/configure-pages) from 4 to 5.
- [Release notes](https://github.com/actions/configure-pages/releases)
- [Commits](https://github.com/actions/configure-pages/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/configure-pages
  dependency-version: '5'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-01-27 19:39:42 +08:00
Xuwznln
2a5ddd611d Upgrade to py 3.11.14; ROS2 Humble 0.7; unilabos 0.10.16
Workbench example, adjust log level, and ci check (#220)

* TestLatency Return Value Example & gitignore update

* Adjust log level & Add workbench virtual example & Add not action decorator & Add check_mode &

* Add CI Check

Fix/workstation yb revision (#217)

* Revert log change & update registry

* Revert opcua client & move electrolyte node

Workstation yb merge dev ready 260113 (#216)

* feat(bioyond): 添加计算实验设计功能,支持化合物配比和滴定比例参数

* feat(bioyond): 添加测量小瓶功能,支持基本参数配置

* feat(bioyond): 添加测量小瓶配置,支持新设备参数

* feat(bioyond): 更新仓库布局和尺寸,支持竖向排列的测量小瓶和试剂存放堆栈

* feat(bioyond): 优化任务创建流程,确保无论成功与否都清理任务队列以避免重复累积

* feat(bioyond): 添加设置反应器温度功能,支持温度范围和异常处理

* feat(bioyond): 调整反应器位置配置,统一坐标格式

* feat(bioyond): 添加调度器启动功能,支持任务队列执行并处理异常

* feat(bioyond): 优化调度器启动功能,添加异常处理并更新相关配置

* feat(opcua): 增强节点ID解析兼容性和数据类型处理

改进节点ID解析逻辑以支持多种格式,包括字符串和数字标识符
添加数据类型转换处理,确保写入值时类型匹配
优化错误提示信息,便于调试节点连接问题

* feat(registry): 新增后处理站的设备配置文件

添加后处理站的YAML配置文件,包含动作映射、状态类型和设备描述

* 添加调度器启动功能,合并物料参数配置,优化物料参数处理逻辑

* 添加从 Bioyond 系统自动同步工作流序列的功能,并更新相关配置

* fix:兼容 BioyondReactionStation 中 workflow_sequence 被重写为 property

* fix:同步工作流序列

* feat: remove commented workflow synchronization from `reaction_station.py`.

* 添加时间约束功能及相关配置

* fix:自动更新物料缓存功能,添加物料时更新缓存并在删除时移除缓存项

* fix:在添加物料时处理字符串和字典返回值,确保正确更新缓存

* fix:更新奔曜错误处理报送为物料变更报送,调整日志记录和响应消息

* feat:添加实验报告简化功能,去除冗余信息并保留关键信息

* feat: 添加任务状态事件发布功能,监控并报告任务运行、超时、完成和错误状态

* fix: 修复添加物料时数据格式错误

* Refactor bioyond_dispensing_station and reaction_station_bioyond YAML configurations

- Removed redundant action value mappings from bioyond_dispensing_station.
- Updated goal properties in bioyond_dispensing_station to use enums for target_stack and other parameters.
- Changed data types for end_point and start_point in reaction_station_bioyond to use string enums (Start, End).
- Simplified descriptions and updated measurement units from μL to mL where applicable.
- Removed unused commands from reaction_station_bioyond to streamline the configuration.

* fix:Change the material unit from μL to mL

* fix:refresh_material_cache

* feat: 动态获取工作流步骤ID,优化工作流配置

* feat: 添加清空服务端所有非核心工作流功能

* fix:修复Bottle类的序列化和反序列化方法

* feat:增强材料缓存更新逻辑,支持处理返回数据中的详细信息

* Add debug log

* feat(workstation): update bioyond config migration and coin cell material search logic

- Migrate bioyond_cell config to JSON structure and remove global variable dependencies
- Implement material search confirmation dialog auto-handling
- Add documentation: 20260113_物料搜寻确认弹窗自动处理功能.md and 20260113_配置迁移修改总结.md

* Refactor module paths for Bioyond devices in YAML configuration files

- Updated the module path for BioyondDispensingStation in bioyond_dispensing_station.yaml to reflect the new directory structure.
- Updated the module path for BioyondReactionStation and BioyondReactor in reaction_station_bioyond.yaml to align with the revised organization of the codebase.

* fix: WareHouse 的不可哈希类型错误,优化父节点去重逻辑

* refactor: Move config from module to instance initialization

* fix: 修正 reaction_station 目录名拼写错误

* feat: Integrate material search logic and cleanup deprecated files

- Update coin_cell_assembly.py with material search dialog handling
- Update YB_warehouses.py with latest warehouse configurations
- Remove outdated documentation and test data files

* Refactor: Use instance attributes for action names and workflow step IDs

* refactor: Split tipbox storage into left and right warehouses

* refactor: Merge tipbox storage left and right into single warehouse

---------

Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
Co-authored-by: Andy6M <xieqiming1132@qq.com>

fix: WareHouse 的不可哈希类型错误,优化父节点去重逻辑

fix parent_uuid fetch when bind_parent_id == node_name

物料更新也是用父节点进行报送

Add None conversion for tube rack etc.

Add set_liquid example.

Add create_resource and test_resource example.

Add restart.
Temp allow action message.

Add no_update_feedback option.

Create session_id by edge.

bump version to 0.10.15

temp cancel update req
2026-01-27 15:21:55 +08:00
Xuwznln
8580b84167 Fix update with different spot and same parent 2026-01-08 03:46:44 +08:00
Xuwznln
3f80349d7d Force update resource when adding new resource / transfer to another resource
location not passed to ItemizedCarrier when assign child resource

Fix size not pass through.
2026-01-08 03:08:01 +08:00
Xuwznln
024156848e Fix build on macos-intel 2026-01-07 21:11:33 +08:00
Xuwznln
8066c200b9 Update README.md
Modify resource_tracker file module path.
2026-01-07 20:54:20 +08:00
Xuwznln
266366cc25 Bump version to 0.10.4 2026-01-07 20:46:23 +08:00
Xuwznln
121c3985cc Update LICENSE 2026-01-07 20:46:23 +08:00
Xuwznln
6ca5c72fc6 Fix drag materials.
Fix and tested new create_resource.

Update create_resource to resource tree mode.
2026-01-07 20:46:23 +08:00
Xianwei Qi
bc8c49ddda test_transfer_liquid 2026-01-07 20:45:41 +08:00
Xuwznln
28f93737ac Close #208. Fix mock devices. 2025-12-28 23:22:50 +08:00
ZiWei
5dc81ec9be bump version to 0.10.3
update registry

do not modify axis globally

Prcix9320 (#207)

* 0.10.7 Update (#101)

* Cleanup registry to be easy-understanding (#76)

* delete deprecated mock devices

* rename categories

* combine chromatographic devices

* rename rviz simulation nodes

* organic virtual devices

* parse vessel_id

* run registry completion before merge

---------

Co-authored-by: Xuwznln <18435084+Xuwznln@users.noreply.github.com>

* fix: workstation handlers and vessel_id parsing

* fix: working dir error when input config path
feat: report publish topic when error

* modify default discovery_interval to 15s

* feat: add trace log level

* feat: 添加ChinWe设备控制类,支持串口通信和电机控制功能 (#79)

* fix: drop_tips not using auto resource select

* fix: discard_tips error

* fix: discard_tips

* fix: prcxi_res

* add: prcxi res
fix: startup slow

* feat: workstation example

* fix pumps and liquid_handler handle

* feat: 优化protocol node节点运行日志

* fix all protocol_compilers and remove deprecated devices

* feat: 新增use_remote_resource参数

* fix and remove redundant info

* bugfixes on organic protocols

* fix filter protocol

* fix protocol node

* 临时兼容错误的driver写法

* fix: prcxi import error

* use call_async in all service to avoid deadlock

* fix: figure_resource

* Update recipe.yaml

* add workstation template and battery example

* feat: add sk & ak

* update workstation base

* Create workstation_architecture.md

* refactor: workstation_base 重构为仅含业务逻辑,通信和子设备管理交给 ProtocolNode

* refactor: ProtocolNode→WorkstationNode

* Add:msgs.action (#83)

* update: Workstation dev 将版本号从 0.10.3 更新为 0.10.4 (#84)

* Add:msgs.action

* update: 将版本号从 0.10.3 更新为 0.10.4

* simplify resource system

* uncompleted refactor

* example for use WorkstationBase

* feat: websocket

* feat: websocket test

* feat: workstation example

* feat: action status

* fix: station自己的方法注册错误

* fix: 还原protocol node处理方法

* fix: build

* fix: missing job_id key

* ws test version 1

* ws test version 2

* ws protocol

* 增加物料关系上传日志

* 增加物料关系上传日志

* 修正物料关系上传

* 修复工站的tracker实例追踪失效问题

* 增加handle检测,增加material edge关系上传

* 修复event loop错误

* 修复edge上报错误

* 修复async错误

* 更新schema的title字段

* 主机节点信息等支持自动刷新

* 注册表编辑器

* 修复status密集发送时,消息出错

* 增加addr参数

* fix: addr param

* fix: addr param

* 取消labid 和 强制config输入

* Add action definitions for LiquidHandlerSetGroup and LiquidHandlerTransferGroup

- Created LiquidHandlerSetGroup.action with fields for group name, wells, and volumes.
- Created LiquidHandlerTransferGroup.action with fields for source and target group names and unit volume.
- Both actions include response fields for return information and success status.

* Add LiquidHandlerSetGroup and LiquidHandlerTransferGroup actions to CMakeLists

* Add set_group and transfer_group methods to PRCXI9300Handler and update liquid_handler.yaml

* result_info改为字典类型

* 新增uat的地址替换

* runze multiple pump support

(cherry picked from commit 49354fcf39)

* remove runze multiple software obtainer

(cherry picked from commit 8bcc92a394)

* support multiple backbone

(cherry picked from commit 4771ff2347)

* Update runze pump format

* Correct runze multiple backbone

* Update runze_multiple_backbone

* Correct runze pump multiple receive method.

* Correct runze pump multiple receive method.

* 对于PRCXI9320的transfer_group,一对多和多对多

* 移除MQTT,更新launch文档,提供注册表示例文件,更新到0.10.5

* fix import error

* fix dupe upload registry

* refactor ws client

* add server timeout

* Fix: run-column with correct vessel id (#86)

* fix run_column

* Update run_column_protocol.py

(cherry picked from commit e5aa4d940a)

* resource_update use resource_add

* 新增版位推荐功能

* 重新规定了版位推荐的入参

* update registry with nested obj

* fix protocol node log_message, added create_resource return value

* fix protocol node log_message, added create_resource return value

* try fix add protocol

* fix resource_add

* 修复移液站错误的aspirate注册表

* Feature/xprbalance-zhida (#80)

* feat(devices): add Zhida GC/MS pretreatment automation workstation

* feat(devices): add mettler_toledo xpr balance

* balance

* 重新补全zhida注册表

* PRCXI9320 json

* PRCXI9320 json

* PRCXI9320 json

* fix resource download

* remove class for resource

* bump version to 0.10.6

* 更新所有注册表

* 修复protocolnode的兼容性

* 修复protocolnode的兼容性

* Update install md

* Add Defaultlayout

* 更新物料接口

* fix dict to tree/nested-dict converter

* coin_cell_station draft

* refactor: rename "station_resource" to "deck"

* add standardized BIOYOND resources: bottle_carrier, bottle

* refactor and add BIOYOND resources tests

* add BIOYOND deck assignment and pass all tests

* fix: update resource with correct structure; remove deprecated liquid_handler set_group action

* feat: 将新威电池测试系统驱动与配置文件并入 workstation_dev_YB2 (#92)

* feat: 新威电池测试系统驱动与注册文件

* feat: bring neware driver & battery.json into workstation_dev_YB2

* add bioyond studio draft

* bioyond station with communication init and resource sync

* fix bioyond station and registry

* fix: update resource with correct structure; remove deprecated liquid_handler set_group action

* frontend_docs

* create/update resources with POST/PUT for big amount/ small amount data

* create/update resources with POST/PUT for big amount/ small amount data

* refactor: add itemized_carrier instead of carrier consists of ResourceHolder

* create warehouse by factory func

* update bioyond launch json

* add child_size for itemized_carrier

* fix bioyond resource io

* Workstation templates: Resources and its CRUD, and workstation tasks (#95)

* coin_cell_station draft

* refactor: rename "station_resource" to "deck"

* add standardized BIOYOND resources: bottle_carrier, bottle

* refactor and add BIOYOND resources tests

* add BIOYOND deck assignment and pass all tests

* fix: update resource with correct structure; remove deprecated liquid_handler set_group action

* feat: 将新威电池测试系统驱动与配置文件并入 workstation_dev_YB2 (#92)

* feat: 新威电池测试系统驱动与注册文件

* feat: bring neware driver & battery.json into workstation_dev_YB2

* add bioyond studio draft

* bioyond station with communication init and resource sync

* fix bioyond station and registry

* create/update resources with POST/PUT for big amount/ small amount data

* refactor: add itemized_carrier instead of carrier consists of ResourceHolder

* create warehouse by factory func

* update bioyond launch json

* add child_size for itemized_carrier

* fix bioyond resource io

---------

Co-authored-by: h840473807 <47357934+h840473807@users.noreply.github.com>
Co-authored-by: Xie Qiming <97236197+Andy6M@users.noreply.github.com>

* 更新物料接口

* Workstation dev yb2 (#100)

* Refactor and extend reaction station action messages

* Refactor dispensing station tasks to enhance parameter clarity and add batch processing capabilities

- Updated `create_90_10_vial_feeding_task` to include detailed parameters for 90%/10% vial feeding, improving clarity and usability.
- Introduced `create_batch_90_10_vial_feeding_task` for batch processing of 90%/10% vial feeding tasks with JSON formatted input.
- Added `create_batch_diamine_solution_task` for batch preparation of diamine solution, also utilizing JSON formatted input.
- Refined `create_diamine_solution_task` to include additional parameters for better task configuration.
- Enhanced schema descriptions and default values for improved user guidance.

* 修复to_plr_resources

* add update remove

* 支持选择器注册表自动生成
支持转运物料

* 修复资源添加

* 修复transfer_resource_to_another生成

* 更新transfer_resource_to_another参数,支持spot入参

* 新增test_resource动作

* fix host_node error

* fix host_node test_resource error

* fix host_node test_resource error

* 过滤本地动作

* 移动内部action以兼容host node

* 修复同步任务报错不显示的bug

* feat: 允许返回非本节点物料,后面可以通过decoration进行区分,就不进行warning了

* update todo

* modify bioyond/plr converter, bioyond resource registry, and tests

* pass the tests

* update todo

* add conda-pack-build.yml

* add auto install script for conda-pack-build.yml

(cherry picked from commit 172599adcf)

* update conda-pack-build.yml

* update conda-pack-build.yml

* update conda-pack-build.yml

* update conda-pack-build.yml

* update conda-pack-build.yml

* Add version in __init__.py
Update conda-pack-build.yml
Add create_zip_archive.py

* Update conda-pack-build.yml

* Update conda-pack-build.yml (with mamba)

* Update conda-pack-build.yml

* Fix FileNotFoundError

* Try fix 'charmap' codec can't encode characters in position 16-23: character maps to <undefined>

* Fix unilabos msgs search error

* Fix environment_check.py

* Update recipe.yaml

* Update registry. Update uuid loop figure method. Update install docs.

* Fix nested conda pack

* Fix one-key installation path error

* Bump version to 0.10.7

* Workshop bj (#99)

* Add LaiYu Liquid device integration and tests

Introduce LaiYu Liquid device implementation, including backend, controllers, drivers, configuration, and resource files. Add hardware connection, tip pickup, and simplified test scripts, as well as experiment and registry configuration for LaiYu Liquid. Documentation and .gitignore for the device are also included.

* feat(LaiYu_Liquid): 重构设备模块结构并添加硬件文档

refactor: 重新组织LaiYu_Liquid模块目录结构
docs: 添加SOPA移液器和步进电机控制指令文档
fix: 修正设备配置中的最大体积默认值
test: 新增工作台配置测试用例
chore: 删除过时的测试脚本和配置文件

* add

* 重构: 将 LaiYu_Liquid.py 重命名为 laiyu_liquid_main.py 并更新所有导入引用

- 使用 git mv 将 LaiYu_Liquid.py 重命名为 laiyu_liquid_main.py
- 更新所有相关文件中的导入引用
- 保持代码功能不变,仅改善命名一致性
- 测试确认所有导入正常工作

* 修复: 在 core/__init__.py 中添加 LaiYuLiquidBackend 导出

- 添加 LaiYuLiquidBackend 到导入列表
- 添加 LaiYuLiquidBackend 到 __all__ 导出列表
- 确保所有主要类都可以正确导入

* 修复大小写文件夹名字

* 电池装配工站二次开发教程(带目录)上传至dev (#94)

* 电池装配工站二次开发教程

* Update intro.md

* 物料教程

* 更新物料教程,json格式注释

* Update prcxi driver & fix transfer_liquid mix_times (#90)

* Update prcxi driver & fix transfer_liquid mix_times

* fix: correct mix_times type

* Update liquid_handler registry

* test: prcxi.py

* Update registry from pr

* fix ony-key script not exist

* clean files

---------

Co-authored-by: Junhan Chang <changjh@dp.tech>
Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
Co-authored-by: Guangxin Zhang <guangxin.zhang.bio@gmail.com>
Co-authored-by: Xie Qiming <97236197+Andy6M@users.noreply.github.com>
Co-authored-by: h840473807 <47357934+h840473807@users.noreply.github.com>
Co-authored-by: LccLink <1951855008@qq.com>
Co-authored-by: lixinyu1011 <61094742+lixinyu1011@users.noreply.github.com>
Co-authored-by: shiyubo0410 <shiyubo@dp.tech>

* fix startup env check.
add auto install during one-key installation

* Try fix one-key build on linux

* Complete all one key installation

* fix: rename schema field to resource_schema with serialization and validation aliases (#104)

Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>

* Fix one-key installation build

Install conda-pack before pack command

Add conda-pack to base when building one-key installer

Fix param error when using mamba run

Try fix one-key build on linux

* Fix conda pack on windows

* add plr_to_bioyond, and refactor bioyond stations

* modify default config

* Fix one-key installation build for windows

* Fix workstation startup
Update registry

* Fix/resource UUID and doc fix (#109)

* Fix ResourceTreeSet load error

* Raise error when using unsupported type to create ResourceTreeSet

* Fix children key error

* Fix children key error

* Fix workstation resource not tracking

* Fix workstation deck & children resource dupe

* Fix workstation deck & children resource dupe

* Fix multiple resource error

* Fix resource tree update

* Fix resource tree update

* Force confirm uuid

* Tip more error log

* Refactor Bioyond workstation and experiment workflow (#105)

Refactored the Bioyond workstation classes to improve parameter handling and workflow management. Updated experiment.py to use BioyondReactionStation with deck and material mappings, and enhanced workflow step parameter mapping and execution logic. Adjusted JSON experiment configs, improved workflow sequence handling, and added UUID assignment to PLR materials. Removed unused station_config and material cache logic, and added detailed docstrings and debug output for workflow methods.

* Fix resource get.
Fix resource parent not found.
Mapping uuid for all resources.

* mount parent uuid

* Add logging configuration based on BasicConfig in main function

* fix workstation node error

* fix workstation node error

* Update boot example

* temp fix for resource get

* temp fix for resource get

* provide error info when cant find plr type

* pack repo info

* fix to plr type error

* fix to plr type error

* Update regular container method

* support no size init

* fix comprehensive_station.json

* fix comprehensive_station.json

* fix type conversion

* fix state loading for regular container

* Update deploy-docs.yml

* Update deploy-docs.yml

---------

Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>

* Close #107
Update doc url.

* Fix/update resource (#112)

* cancel upload_registry

* Refactor Bioyond workstation and experiment workflow -fix (#111)

* refactor(bioyond_studio): 优化材料缓存加载和参数验证逻辑

改进材料缓存加载逻辑以支持多种材料类型和详细材料处理
更新工作流参数验证中的字段名从key/value改为Key/DisplayValue
移除未使用的merge_workflow_with_parameters方法
添加get_station_info方法获取工作站基础信息
清理实验文件中的注释代码和更新导入路径

* fix: 修复资源移除时的父资源检查问题

在BaseROS2DeviceNode中,移除资源前添加对父资源是否为None的检查,避免空指针异常
同时更新Bottle和BottleCarrier类以支持**kwargs参数
修正测试文件中Liquid_feeding_beaker的大小写拼写错误

* correct return message

---------

Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>

* fix resource_get in action

* fix(reaction_station): 清空工作流序列和参数避免重复执行 (#113)

在创建任务后清空工作流序列和参数,防止下次执行时累积重复

* Update create_resource device_id

* Update ResourceTracker

add more enumeration in POSE

fix converter in resource_tracker

* Update graphio together with workstation design.

fix(reaction_station): 为步骤参数添加Value字段传个BY后端

fix(bioyond/warehouses): 修正仓库尺寸和物品排列参数

调整仓库的x轴和z轴物品数量以及物品尺寸参数,使其符合4x1x4的规格要求

fix warehouse serialize/deserialize

fix bioyond converter

fix itemized_carrier.unassign_child_resource

allow not-loaded MSG in registry

add layout serializer & converter

warehouseuse A1-D4; add warehouse layout

fix(graphio): 修正bioyond到plr资源转换中的坐标计算错误

Fix resource assignment and type mapping issues

Corrects resource assignment in ItemizedCarrier by using the correct spot key from _ordering. Updates graphio to use 'typeName' instead of 'name' for type mapping in resource_bioyond_to_plr. Renames DummyWorkstation to BioyondWorkstation in workstation_http_service for clarity.

* Update workstation & bioyond example

Refine descriptions in Bioyond reaction station YAML

Updated and clarified field and operation descriptions in the reaction_station_bioyond.yaml file for improved accuracy and consistency. Changes include more precise terminology, clearer parameter explanations, and standardized formatting for operation schemas.

refactor(workstation): 更新反应站参数描述并添加分液站配置文件

修正反应站方法参数描述,使其更准确清晰
添加bioyond_dispensing_station.yaml配置文件

add create_workflow script and test

add invisible_slots to carriers

fix(warehouses): 修正bioyond_warehouse_1x4x4仓库的尺寸参数

调整仓库的num_items_x和num_items_z值以匹配实际布局,并更新物品尺寸参数

save resource get data. allow empty value for layout and cross_section_type

More decks&plates support for bioyond (#115)

refactor(registry): 重构反应站设备配置,简化并更新操作命令

移除旧的自动操作命令,新增针对具体化学操作的命令配置
更新模块路径和配置结构,优化参数定义和描述

fix(dispensing_station): 修正物料信息查询方法调用

将直接调用material_id_query改为通过hardware_interface调用,以符合接口设计规范

* PRCXI Update

修改prcxi连线

prcxi样例图

Create example_prcxi.json

* Update resource extra & uuid.

use ordering to convert identifier to idx

convert identifier to site idx

correct extra key

update extra before transfer

fix multiple instance error

add resource_tree_transfer func

fox itemrized carrier assign child resource

support internal device material transfer

remove extra key

use same callback group

support material extra

support material extra
support update_resource_site in extra

* Update workstation.

modify workstation_architecture docs

bioyond_HR (#133)

* feat: Enhance Bioyond synchronization and resource management

- Implemented synchronization for all material types (consumables, samples, reagents) from Bioyond, logging detailed information for each type.
- Improved error handling and logging during synchronization processes.
- Added functionality to save Bioyond material IDs in UniLab resources for future updates.
- Enhanced the `sync_to_external` method to handle material movements correctly, including querying and creating materials in Bioyond.
- Updated warehouse configurations to support new storage types and improved layout for better resource management.
- Introduced new resource types such as reactors and tip boxes, with detailed specifications.
- Modified warehouse factory to support column offsets for naming conventions (e.g., A05-D08).
- Improved resource tracking by merging extra attributes instead of overwriting them.
- Added a new method for updating resources in Bioyond, ensuring better synchronization of resource changes.

* feat: 添加TipBox和Reactor的配置到bottles.yaml

* fix: 修复液体投料方法中的volume参数处理逻辑

修复solid_feeding_vials方法中的volume参数处理逻辑,优化solvents参数的使用条件

更新液体投料方法,支持通过溶剂信息自动计算体积,添加solvents参数并更新文档描述

Add batch creation methods for vial and solution tasks

添加批量创建90%10%小瓶投料任务和二胺溶液配置任务的功能,更新相关参数和默认值

* 封膜仪、撕膜仪、耗材站接口

* 添加Raman和xrd相关代码

* Resource update & asyncio fix

correct bioyond config

prcxi example

fix append_resource

fix regularcontainer

fix cancel error

fix resource_get param

fix json dumps

support name change during materials change

enable slave mode

change uuid logger to trace level

correct remove_resource stats

disable slave connect websocket

adjust with_children param

modify devices to use correct executor (sleep, create_task)

support sleep and create_task in node

fix run async execution error

* bump version to 0.10.9

update registry

* PRCXI Reset Error Correction (#166)

* change 9320 desk row number to 4

* Updated 9320 host address

* Updated 9320 host address

* Add **kwargs in classes: PRCXI9300Deck and PRCXI9300Container

* Removed all sample_id in prcxi_9320.json to avoid KeyError

* 9320 machine testing settings

* Typo

* Rewrite setup logic to clear error code

* 初始化 step_mode 属性

* 1114物料手册定义教程byxinyu (#165)

* 宜宾奔耀工站deck前端by_Xinyu

* 构建物料教程byxinyu

* 1114物料手册定义教程

* 3d sim (#97)

* 修改lh的json启动

* 修改lh的json启动

* 修改backend,做成sim的通用backend

* 修改yaml的地址,3D模型适配网页生产环境

* 添加laiyu硬件连接

* 修改移液枪的状态判断方法,

修改移液枪的状态判断方法,
添加三轴的表定点与零点之间的转换
添加三轴真实移动的backend

* 修改laiyu移液站

简化移动方法,
取消软件限制位置,
修改当值使用Z轴时也需要重新复位Z轴的问题

* 更新lh以及laiyu workshop

1,现在可以直接通过修改backend,适配其他的移液站,主类依旧使用LiquidHandler,不用重新编写

2,修改枪头判断标准,使用枪头自身判断而不是类的判断,

3,将归零参数用毫米计算,方便手动调整,

4,修改归零方式,上电使用机械归零,确定机械零点,手动归零设置工作区域零点方便计算,二者互不干涉

* 修改枪头动作

* 修改虚拟仿真方法

---------

Co-authored-by: zhangshixiang <@zhangshixiang>
Co-authored-by: Junhan Chang <changjh@dp.tech>

* 标准化opcua设备接入unilab (#78)

* 初始提交,只保留工作区当前状态

* remove redundant arm_slider meshes

---------

Co-authored-by: Junhan Chang <changjh@dp.tech>

* add new laiyu liquid driver, yaml and json files (#164)

* HR物料同步,前端展示位置修复 (#135)

* 更新Bioyond工作站配置,添加新的物料类型映射和载架定义,优化物料查询逻辑

* 添加Bioyond实验配置文件,定义物料类型映射和设备配置

* 更新bioyond_warehouse_reagent_stack方法,修正试剂堆栈尺寸和布局描述

* 更新Bioyond实验配置,修正物料类型映射,优化设备配置

* 更新Bioyond资源同步逻辑,优化物料入库流程,增强错误处理和日志记录

* 更新Bioyond资源,添加配液站和反应站专用载架,优化仓库工厂函数的排序方式

* 更新Bioyond资源,添加配液站和反应站相关载架,优化试剂瓶和样品瓶配置

* 更新Bioyond实验配置,修正试剂瓶载架ID,确保与设备匹配

* 更新Bioyond资源,移除反应站单烧杯载架,添加反应站单烧瓶载架分类

* Refactor Bioyond resource synchronization and update bottle carrier definitions

- Removed traceback printing in error handling for Bioyond synchronization.
- Enhanced logging for existing Bioyond material ID usage during synchronization.
- Added new bottle carrier definitions for single flask and updated existing ones.
- Refactored dispensing station and reaction station bottle definitions for clarity and consistency.
- Improved resource mapping and error handling in graphio for Bioyond resource conversion.
- Introduced layout parameter in warehouse factory for better warehouse configuration.

* 更新Bioyond仓库工厂,添加排序方式支持,优化坐标计算逻辑

* 更新Bioyond载架和甲板配置,调整样品板尺寸和仓库坐标

* 更新Bioyond资源同步,增强占用位置日志信息,修正坐标转换逻辑

* 更新Bioyond反应站和分配站配置,调整材料类型映射和ID,移除不必要的项

* support name change during materials change

* fix json dumps

* correct tip

* 优化调度器API路径,更新相关方法描述

* 更新 BIOYOND 载架相关文档,调整 API 以支持自带试剂瓶的载架类型,修复资源获取时的子物料处理逻辑

* 实现资源删除时的同步处理,优化出库操作逻辑

* 修复 ItemizedCarrier 中的可见性逻辑

* 保存 Bioyond 原始信息到 unilabos_extra,以便出库时查询

* 根据 resource.capacity 判断是试剂瓶(载架)还是多瓶载架,走不同的奔曜转换

* Fix bioyond bottle_carriers ordering

* 优化 Bioyond 物料同步逻辑,增强坐标解析和位置更新处理

* disable slave connect websocket

* correct remove_resource stats

* change uuid logger to trace level

* enable slave mode

* refactor(bioyond): 统一资源命名并优化物料同步逻辑

- 将DispensingStation和ReactionStation资源统一为PolymerStation命名
- 优化物料同步逻辑,支持耗材类型(typeMode=0)的查询
- 添加物料默认参数配置功能
- 调整仓库坐标布局
- 清理废弃资源定义

* feat(warehouses): 为仓库函数添加col_offset和layout参数

* refactor: 更新实验配置中的物料类型映射命名

将DispensingStation和ReactionStation的物料类型映射统一更名为PolymerStation,保持命名一致性

* fix: 更新实验配置中的载体名称从6VialCarrier到6StockCarrier

* feat(bioyond): 实现物料创建与入库分离逻辑

将物料同步流程拆分为两个独立阶段:transfer阶段只创建物料,add阶段执行入库
简化状态检查接口,仅返回连接状态

* fix(reaction_station): 修正液体进料烧杯体积单位并增强返回结果

将液体进料烧杯的体积单位从μL改为g以匹配实际使用场景
在返回结果中添加merged_workflow和order_params字段,提供更完整的工作流信息

* feat(dispensing_station): 在任务创建返回结果中添加order_params信息

在create_order方法返回结果中增加order_params字段,以便调用方获取完整的任务参数

* fix(dispensing_station): 修改90%物料分配逻辑从分成3份改为直接使用

原逻辑将主称固体平均分成3份作为90%物料,现改为直接使用main_portion

* feat(bioyond): 添加任务编码和任务ID的输出,支持批量任务创建后的状态监控

* refactor(registry): 简化设备配置中的任务结果处理逻辑

将多个单独的任务编码和ID字段合并为统一的return_info字段
更新相关描述以反映新的数据结构

* feat(工作站): 添加HTTP报送服务和任务完成状态跟踪

- 在graphio.py中添加API必需字段
- 实现工作站HTTP服务启动和停止逻辑
- 添加任务完成状态跟踪字典和等待方法
- 重写任务完成报送处理方法记录状态
- 支持批量任务完成等待和报告获取

* refactor(dispensing_station): 移除wait_for_order_completion_and_get_report功能

该功能已被wait_for_multiple_orders_and_get_reports替代,简化代码结构

* fix: 更新任务报告API错误

* fix(workstation_http_service): 修复状态查询中device_id获取逻辑

处理状态查询时安全获取device_id,避免因属性不存在导致的异常

* fix(bioyond_studio): 改进物料入库失败时的错误处理和日志记录

在物料入库API调用失败时,添加更详细的错误信息打印
同时修正station.py中对空响应和失败情况的判断逻辑

* refactor(bioyond): 优化瓶架载体的分配逻辑和注释说明

重构瓶架载体的分配逻辑,使用嵌套循环替代硬编码索引分配
添加更详细的坐标映射说明,明确PLR与Bioyond坐标的对应关系

* fix(bioyond_rpc): 修复物料入库成功时无data字段返回空的问题

当API返回成功但无data字段时,返回包含success标识的字典而非空字典

---------

Co-authored-by: Xuwznln <18435084+Xuwznln@users.noreply.github.com>
Co-authored-by: Junhan Chang <changjh@dp.tech>

* nmr

* Update devices

* bump version to 0.10.10

* Update repo files.

* Add get_resource_with_dir & get_resource method

* fix camera & workstation & warehouse & reaction station driver

* update docs, test examples
fix liquid_handler init bug

* bump version to 0.10.11

* Add startup_json_path, disable_browser, port config

* Update oss config

* feat(bioyond_studio): 添加项目API接口支持及优化物料管理功能

添加通用项目API接口方法(_post_project_api, _delete_project_api)用于与LIMS系统交互
实现compute_experiment_design方法用于实验设计计算
新增brief_step_parameters等订单相关接口方法
优化物料转移逻辑,增加异步任务处理
扩展BioyondV1RPC类,添加批量物料操作、订单状态管理等功能

* feat(bioyond): 添加测量小瓶仓库和更新仓库工厂函数参数

* Support unilabos_samples key

* add session_id and normal_exit

* Add result schema and add TypedDict conversion.

* Fix port error

* Add backend api and update doc

* Add get_regular_container func

* Add get_regular_container func

* Transfer_liquid (#176)

* change 9320 desk row number to 4

* Updated 9320 host address

* Updated 9320 host address

* Add **kwargs in classes: PRCXI9300Deck and PRCXI9300Container

* Removed all sample_id in prcxi_9320.json to avoid KeyError

* 9320 machine testing settings

* Typo

* Typo in base_device_node.py

* Enhance liquid handling functionality by adding support for multiple transfer modes (one-to-many, one-to-one, many-to-one) and improving parameter validation. Default channel usage is set when not specified. Adjusted mixing logic to ensure it only occurs when valid conditions are met. Updated documentation for clarity.

* Auto dump logs, fix workstation input schema

* Fix startup with remote resource error

Resource dict fully change to "pose" key

Update oss link

Reduce pylabrobot conversion warning & force enable log dump.

更新 logo 图片

* signal when host node is ready

* fix ros2 future

print all logs to file
fix resource dict dump error

* update version to 0.10.12

* 修改sample_uuid的返回值

* 修改pose标签设定机制

* 添加 aspiate函数返回值

* 返回dispense后的sample_uuid

* 添加self.pending_liquids_dict的重置方法

* 修改prcxi的json文件,解决trach错误问题

* 修改prcxijson,防止PlateT4的硬件错误

* 对laiyu移液站进行部分修改,取消多次初始化的问题

* 修改根据新的物料格式,修改可视化

* 添加切换枪头方法,添加mock振荡与加热方法

* 夹爪添加

* 删除多余的laiyu部分

* 云端可启动夹爪

* Delete __init__.py

* Enhance PRCXI9300 classes with new Container and TipRack implementations, improving state management and initialization logic. Update JSON configuration to reflect type changes for containers and plates.

* 修改上传数据

---------

Co-authored-by: Junhan Chang <changjh@dp.tech>
Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
Co-authored-by: Guangxin Zhang <guangxin.zhang.bio@gmail.com>
Co-authored-by: Xie Qiming <97236197+Andy6M@users.noreply.github.com>
Co-authored-by: h840473807 <47357934+h840473807@users.noreply.github.com>
Co-authored-by: LccLink <1951855008@qq.com>
Co-authored-by: lixinyu1011 <61094742+lixinyu1011@users.noreply.github.com>
Co-authored-by: shiyubo0410 <shiyubo@dp.tech>
Co-authored-by: hh.(SII) <103566763+Mile-Away@users.noreply.github.com>
Co-authored-by: Xianwei Qi <qxw@stu.pku.edu.cn>
Co-authored-by: WenzheG <wenzheguo32@gmail.com>
Co-authored-by: Harry Liu <113173203+ALITTLELZ@users.noreply.github.com>
Co-authored-by: q434343 <73513873+q434343@users.noreply.github.com>
Co-authored-by: tt <166512503+tt11142023@users.noreply.github.com>
Co-authored-by: xyc <49015816+xiaoyu10031@users.noreply.github.com>
Co-authored-by: zhangshixiang <@zhangshixiang>
Co-authored-by: zhangshixiang <554662886@qq.com>
Co-authored-by: ALITTLELZ <l_LZlz@163.com>

Add topic config

add camera driver (#191)

* add camera driver

* add init.py file to cameraSII driver

增强新威电池测试系统 OSS 上传功能 / Enhanced Neware Battery Test System OSS Upload (#196)

* feat: neware-oss-upload-enhancement

* feat(neware): enhance OSS upload with metadata and workflow handles

Add post process station and related resources (#195)

* Add post process station and related resources

- Created JSON configuration for post_process_station and its child post_process_deck.
- Added YAML definitions for post_process_station, bottle carriers, bottles, and deck resources.
- Implemented Python classes for bottle carriers, bottles, decks, and warehouses to manage resources in the post process.
- Established a factory method for creating warehouses with customizable dimensions and layouts.
- Defined the structure and behavior of the post_process_deck and its associated warehouses.

* feat(post_process): add post_process_station and related warehouse functionality

- Introduced post_process_station.json to define the post-processing station structure.
- Implemented post_process_warehouse.py to create warehouse configurations with customizable layouts.
- Added warehouses.py for specific warehouse configurations (4x3x1).
- Updated post_process_station.yaml to reflect new module paths for OpcUaClient.
- Refactored bottle carriers and bottles YAML files to point to the new module paths.
- Adjusted deck.yaml to align with the new organizational structure for post_process_deck.

prcxi resource (#202)

* prcxi resource

* prcxi_resource

* Fix upload error not showing.
Support str type category.

---------

Co-authored-by: Xuwznln <18435084+Xuwznln@users.noreply.github.com>

Fix upload error not showing.
Support str type category.

feat: introduce `wait_time` command and configurable device communication timeout.

feat: Add `SyringePump` (SY-03B) driver with unified serial/TCP transport for `chinwe` device, including registry and test configurations.
2025-12-26 03:36:48 +08:00
Xuwznln
13a6795657 Update organic syn station. 2025-12-15 02:34:36 +08:00
Xianwei Qi
53219d8b04 Update docs
update "laiyu" missing init file.

fix "laiyu" missing init file.

fix "🐛 fix"

🐛 fix: config file is overwrited by default args even if not be set.

mix

修改了mix,仿真流程报错问题
2025-12-14 13:13:21 +08:00
Xuwznln
b1cdef9185 update version to 0.10.12 2025-12-04 18:47:16 +08:00
Xuwznln
9854ed8c9c fix ros2 future
print all logs to file
fix resource dict dump error
2025-12-04 18:46:37 +08:00
Xuwznln
52544a2c69 signal when host node is ready 2025-12-02 12:00:26 +08:00
ZiWei
5ce433e235 Fix startup with remote resource error
Resource dict fully change to "pose" key

Update oss link

Reduce pylabrobot conversion warning & force enable log dump.

更新 logo 图片
2025-12-02 11:51:01 +08:00
Xuwznln
c7c14d2332 Auto dump logs, fix workstation input schema 2025-11-27 14:24:40 +08:00
Harry Liu
6fdd482649 Transfer_liquid (#176)
* change 9320 desk row number to 4

* Updated 9320 host address

* Updated 9320 host address

* Add **kwargs in classes: PRCXI9300Deck and PRCXI9300Container

* Removed all sample_id in prcxi_9320.json to avoid KeyError

* 9320 machine testing settings

* Typo

* Typo in base_device_node.py

* Enhance liquid handling functionality by adding support for multiple transfer modes (one-to-many, one-to-one, many-to-one) and improving parameter validation. Default channel usage is set when not specified. Adjusted mixing logic to ensure it only occurs when valid conditions are met. Updated documentation for clarity.
2025-11-27 13:49:04 +08:00
Xuwznln
d390236318 Add get_regular_container func 2025-11-27 13:47:12 +08:00
Xuwznln
ed8ee29732 Add get_regular_container func 2025-11-27 13:46:40 +08:00
Xuwznln
ffc583e9d5 Add backend api and update doc 2025-11-26 19:17:46 +08:00
Xuwznln
f1ad0c9c96 Fix port error 2025-11-25 15:19:15 +08:00
Xuwznln
8fa3407649 Add result schema and add TypedDict conversion. 2025-11-25 15:16:27 +08:00
Xuwznln
d3282822fc add session_id and normal_exit 2025-11-20 22:43:24 +08:00
Xuwznln
554bcade24 Support unilabos_samples key 2025-11-19 15:53:59 +08:00
ZiWei
a662c75de1 feat(bioyond): 添加测量小瓶仓库和更新仓库工厂函数参数 2025-11-19 14:26:12 +08:00
ZiWei
931614fe64 feat(bioyond_studio): 添加项目API接口支持及优化物料管理功能
添加通用项目API接口方法(_post_project_api, _delete_project_api)用于与LIMS系统交互
实现compute_experiment_design方法用于实验设计计算
新增brief_step_parameters等订单相关接口方法
优化物料转移逻辑,增加异步任务处理
扩展BioyondV1RPC类,添加批量物料操作、订单状态管理等功能
2025-11-19 14:26:10 +08:00
Xuwznln
d39662f65f Update oss config 2025-11-19 14:22:03 +08:00
Xuwznln
acf5fdebf8 Add startup_json_path, disable_browser, port config 2025-11-18 18:59:39 +08:00
Xuwznln
7f7b1c13c0 bump version to 0.10.11 2025-11-18 18:47:26 +08:00
Xuwznln
75f09034ff update docs, test examples
fix liquid_handler init bug
2025-11-18 18:42:27 +08:00
ZiWei
549a50220b fix camera & workstation & warehouse & reaction station driver 2025-11-18 18:41:37 +08:00
Xuwznln
4189a2cfbe Add get_resource_with_dir & get_resource method 2025-11-15 22:50:30 +08:00
Xuwznln
48895a9bb1 Update repo files. 2025-11-15 03:15:44 +08:00
Xuwznln
891f126ed6 bump version to 0.10.10 2025-11-15 03:11:37 +08:00
Xuwznln
4d3475a849 Update devices 2025-11-15 03:11:36 +08:00
WenzheG
b475db66df nmr 2025-11-15 03:11:35 +08:00
ZiWei
a625a86e3e HR物料同步,前端展示位置修复 (#135)
* 更新Bioyond工作站配置,添加新的物料类型映射和载架定义,优化物料查询逻辑

* 添加Bioyond实验配置文件,定义物料类型映射和设备配置

* 更新bioyond_warehouse_reagent_stack方法,修正试剂堆栈尺寸和布局描述

* 更新Bioyond实验配置,修正物料类型映射,优化设备配置

* 更新Bioyond资源同步逻辑,优化物料入库流程,增强错误处理和日志记录

* 更新Bioyond资源,添加配液站和反应站专用载架,优化仓库工厂函数的排序方式

* 更新Bioyond资源,添加配液站和反应站相关载架,优化试剂瓶和样品瓶配置

* 更新Bioyond实验配置,修正试剂瓶载架ID,确保与设备匹配

* 更新Bioyond资源,移除反应站单烧杯载架,添加反应站单烧瓶载架分类

* Refactor Bioyond resource synchronization and update bottle carrier definitions

- Removed traceback printing in error handling for Bioyond synchronization.
- Enhanced logging for existing Bioyond material ID usage during synchronization.
- Added new bottle carrier definitions for single flask and updated existing ones.
- Refactored dispensing station and reaction station bottle definitions for clarity and consistency.
- Improved resource mapping and error handling in graphio for Bioyond resource conversion.
- Introduced layout parameter in warehouse factory for better warehouse configuration.

* 更新Bioyond仓库工厂,添加排序方式支持,优化坐标计算逻辑

* 更新Bioyond载架和甲板配置,调整样品板尺寸和仓库坐标

* 更新Bioyond资源同步,增强占用位置日志信息,修正坐标转换逻辑

* 更新Bioyond反应站和分配站配置,调整材料类型映射和ID,移除不必要的项

* support name change during materials change

* fix json dumps

* correct tip

* 优化调度器API路径,更新相关方法描述

* 更新 BIOYOND 载架相关文档,调整 API 以支持自带试剂瓶的载架类型,修复资源获取时的子物料处理逻辑

* 实现资源删除时的同步处理,优化出库操作逻辑

* 修复 ItemizedCarrier 中的可见性逻辑

* 保存 Bioyond 原始信息到 unilabos_extra,以便出库时查询

* 根据 resource.capacity 判断是试剂瓶(载架)还是多瓶载架,走不同的奔曜转换

* Fix bioyond bottle_carriers ordering

* 优化 Bioyond 物料同步逻辑,增强坐标解析和位置更新处理

* disable slave connect websocket

* correct remove_resource stats

* change uuid logger to trace level

* enable slave mode

* refactor(bioyond): 统一资源命名并优化物料同步逻辑

- 将DispensingStation和ReactionStation资源统一为PolymerStation命名
- 优化物料同步逻辑,支持耗材类型(typeMode=0)的查询
- 添加物料默认参数配置功能
- 调整仓库坐标布局
- 清理废弃资源定义

* feat(warehouses): 为仓库函数添加col_offset和layout参数

* refactor: 更新实验配置中的物料类型映射命名

将DispensingStation和ReactionStation的物料类型映射统一更名为PolymerStation,保持命名一致性

* fix: 更新实验配置中的载体名称从6VialCarrier到6StockCarrier

* feat(bioyond): 实现物料创建与入库分离逻辑

将物料同步流程拆分为两个独立阶段:transfer阶段只创建物料,add阶段执行入库
简化状态检查接口,仅返回连接状态

* fix(reaction_station): 修正液体进料烧杯体积单位并增强返回结果

将液体进料烧杯的体积单位从μL改为g以匹配实际使用场景
在返回结果中添加merged_workflow和order_params字段,提供更完整的工作流信息

* feat(dispensing_station): 在任务创建返回结果中添加order_params信息

在create_order方法返回结果中增加order_params字段,以便调用方获取完整的任务参数

* fix(dispensing_station): 修改90%物料分配逻辑从分成3份改为直接使用

原逻辑将主称固体平均分成3份作为90%物料,现改为直接使用main_portion

* feat(bioyond): 添加任务编码和任务ID的输出,支持批量任务创建后的状态监控

* refactor(registry): 简化设备配置中的任务结果处理逻辑

将多个单独的任务编码和ID字段合并为统一的return_info字段
更新相关描述以反映新的数据结构

* feat(工作站): 添加HTTP报送服务和任务完成状态跟踪

- 在graphio.py中添加API必需字段
- 实现工作站HTTP服务启动和停止逻辑
- 添加任务完成状态跟踪字典和等待方法
- 重写任务完成报送处理方法记录状态
- 支持批量任务完成等待和报告获取

* refactor(dispensing_station): 移除wait_for_order_completion_and_get_report功能

该功能已被wait_for_multiple_orders_and_get_reports替代,简化代码结构

* fix: 更新任务报告API错误

* fix(workstation_http_service): 修复状态查询中device_id获取逻辑

处理状态查询时安全获取device_id,避免因属性不存在导致的异常

* fix(bioyond_studio): 改进物料入库失败时的错误处理和日志记录

在物料入库API调用失败时,添加更详细的错误信息打印
同时修正station.py中对空响应和失败情况的判断逻辑

* refactor(bioyond): 优化瓶架载体的分配逻辑和注释说明

重构瓶架载体的分配逻辑,使用嵌套循环替代硬编码索引分配
添加更详细的坐标映射说明,明确PLR与Bioyond坐标的对应关系

* fix(bioyond_rpc): 修复物料入库成功时无data字段返回空的问题

当API返回成功但无data字段时,返回包含success标识的字典而非空字典

---------

Co-authored-by: Xuwznln <18435084+Xuwznln@users.noreply.github.com>
Co-authored-by: Junhan Chang <changjh@dp.tech>
2025-11-15 03:11:34 +08:00
xyc
37e0f1037c add new laiyu liquid driver, yaml and json files (#164) 2025-11-15 03:11:33 +08:00
tt
a242253145 标准化opcua设备接入unilab (#78)
* 初始提交,只保留工作区当前状态

* remove redundant arm_slider meshes

---------

Co-authored-by: Junhan Chang <changjh@dp.tech>
2025-11-15 03:11:31 +08:00
q434343
448e0074b7 3d sim (#97)
* 修改lh的json启动

* 修改lh的json启动

* 修改backend,做成sim的通用backend

* 修改yaml的地址,3D模型适配网页生产环境

* 添加laiyu硬件连接

* 修改移液枪的状态判断方法,

修改移液枪的状态判断方法,
添加三轴的表定点与零点之间的转换
添加三轴真实移动的backend

* 修改laiyu移液站

简化移动方法,
取消软件限制位置,
修改当值使用Z轴时也需要重新复位Z轴的问题

* 更新lh以及laiyu workshop

1,现在可以直接通过修改backend,适配其他的移液站,主类依旧使用LiquidHandler,不用重新编写

2,修改枪头判断标准,使用枪头自身判断而不是类的判断,

3,将归零参数用毫米计算,方便手动调整,

4,修改归零方式,上电使用机械归零,确定机械零点,手动归零设置工作区域零点方便计算,二者互不干涉

* 修改枪头动作

* 修改虚拟仿真方法

---------

Co-authored-by: zhangshixiang <@zhangshixiang>
Co-authored-by: Junhan Chang <changjh@dp.tech>
2025-11-15 03:11:30 +08:00
lixinyu1011
304827fc8d 1114物料手册定义教程byxinyu (#165)
* 宜宾奔耀工站deck前端by_Xinyu

* 构建物料教程byxinyu

* 1114物料手册定义教程
2025-11-15 03:11:29 +08:00
Harry Liu
872b3d781f PRCXI Reset Error Correction (#166)
* change 9320 desk row number to 4

* Updated 9320 host address

* Updated 9320 host address

* Add **kwargs in classes: PRCXI9300Deck and PRCXI9300Container

* Removed all sample_id in prcxi_9320.json to avoid KeyError

* 9320 machine testing settings

* Typo

* Rewrite setup logic to clear error code

* 初始化 step_mode 属性
2025-11-15 03:11:29 +08:00
Xuwznln
813400f2b4 bump version to 0.10.9
update registry
2025-11-15 02:45:30 +08:00
Xuwznln
b6dfe2b944 Resource update & asyncio fix
correct bioyond config

prcxi example

fix append_resource

fix regularcontainer

fix cancel error

fix resource_get param

fix json dumps

support name change during materials change

enable slave mode

change uuid logger to trace level

correct remove_resource stats

disable slave connect websocket

adjust with_children param

modify devices to use correct executor (sleep, create_task)

support sleep and create_task in node

fix run async execution error
2025-11-15 02:45:12 +08:00
WenzheG
8807865649 添加Raman和xrd相关代码 2025-11-15 02:44:03 +08:00
Guangxin Zhang
5fc7eb7586 封膜仪、撕膜仪、耗材站接口 2025-11-15 02:44:02 +08:00
ZiWei
9bd72b48e1 Update workstation.
modify workstation_architecture docs

bioyond_HR (#133)

* feat: Enhance Bioyond synchronization and resource management

- Implemented synchronization for all material types (consumables, samples, reagents) from Bioyond, logging detailed information for each type.
- Improved error handling and logging during synchronization processes.
- Added functionality to save Bioyond material IDs in UniLab resources for future updates.
- Enhanced the `sync_to_external` method to handle material movements correctly, including querying and creating materials in Bioyond.
- Updated warehouse configurations to support new storage types and improved layout for better resource management.
- Introduced new resource types such as reactors and tip boxes, with detailed specifications.
- Modified warehouse factory to support column offsets for naming conventions (e.g., A05-D08).
- Improved resource tracking by merging extra attributes instead of overwriting them.
- Added a new method for updating resources in Bioyond, ensuring better synchronization of resource changes.

* feat: 添加TipBox和Reactor的配置到bottles.yaml

* fix: 修复液体投料方法中的volume参数处理逻辑

修复solid_feeding_vials方法中的volume参数处理逻辑,优化solvents参数的使用条件

更新液体投料方法,支持通过溶剂信息自动计算体积,添加solvents参数并更新文档描述

Add batch creation methods for vial and solution tasks

添加批量创建90%10%小瓶投料任务和二胺溶液配置任务的功能,更新相关参数和默认值
2025-11-15 02:43:50 +08:00
Xuwznln
42b78ab4c1 Update resource extra & uuid.
use ordering to convert identifier to idx

convert identifier to site idx

correct extra key

update extra before transfer

fix multiple instance error

add resource_tree_transfer func

fox itemrized carrier assign child resource

support internal device material transfer

remove extra key

use same callback group

support material extra

support material extra
support update_resource_site in extra
2025-11-15 02:43:13 +08:00
Xianwei Qi
9645609a05 PRCXI Update
修改prcxi连线

prcxi样例图

Create example_prcxi.json
2025-11-15 02:41:30 +08:00
ZiWei
a2a827d7ac Update workstation & bioyond example
Refine descriptions in Bioyond reaction station YAML

Updated and clarified field and operation descriptions in the reaction_station_bioyond.yaml file for improved accuracy and consistency. Changes include more precise terminology, clearer parameter explanations, and standardized formatting for operation schemas.

refactor(workstation): 更新反应站参数描述并添加分液站配置文件

修正反应站方法参数描述,使其更准确清晰
添加bioyond_dispensing_station.yaml配置文件

add create_workflow script and test

add invisible_slots to carriers

fix(warehouses): 修正bioyond_warehouse_1x4x4仓库的尺寸参数

调整仓库的num_items_x和num_items_z值以匹配实际布局,并更新物品尺寸参数

save resource get data. allow empty value for layout and cross_section_type

More decks&plates support for bioyond (#115)

refactor(registry): 重构反应站设备配置,简化并更新操作命令

移除旧的自动操作命令,新增针对具体化学操作的命令配置
更新模块路径和配置结构,优化参数定义和描述

fix(dispensing_station): 修正物料信息查询方法调用

将直接调用material_id_query改为通过hardware_interface调用,以符合接口设计规范
2025-11-15 02:40:54 +08:00
ZiWei
bb3ca645a4 Update graphio together with workstation design.
fix(reaction_station): 为步骤参数添加Value字段传个BY后端

fix(bioyond/warehouses): 修正仓库尺寸和物品排列参数

调整仓库的x轴和z轴物品数量以及物品尺寸参数,使其符合4x1x4的规格要求

fix warehouse serialize/deserialize

fix bioyond converter

fix itemized_carrier.unassign_child_resource

allow not-loaded MSG in registry

add layout serializer & converter

warehouseuse A1-D4; add warehouse layout

fix(graphio): 修正bioyond到plr资源转换中的坐标计算错误

Fix resource assignment and type mapping issues

Corrects resource assignment in ItemizedCarrier by using the correct spot key from _ordering. Updates graphio to use 'typeName' instead of 'name' for type mapping in resource_bioyond_to_plr. Renames DummyWorkstation to BioyondWorkstation in workstation_http_service for clarity.
2025-11-15 02:39:01 +08:00
Junhan Chang
37ee43d19a Update ResourceTracker
add more enumeration in POSE

fix converter in resource_tracker
2025-11-15 02:38:01 +08:00
Xuwznln
bc30f23e34 Update create_resource device_id 2025-10-20 21:45:20 +08:00
ZiWei
166d84afe1 fix(reaction_station): 清空工作流序列和参数避免重复执行 (#113)
在创建任务后清空工作流序列和参数,防止下次执行时累积重复
2025-10-17 13:44:36 +08:00
Junhan Chang
1b43c53015 fix resource_get in action 2025-10-17 13:44:35 +08:00
Xuwznln
d4415f5a35 Fix/update resource (#112)
* cancel upload_registry

* Refactor Bioyond workstation and experiment workflow -fix (#111)

* refactor(bioyond_studio): 优化材料缓存加载和参数验证逻辑

改进材料缓存加载逻辑以支持多种材料类型和详细材料处理
更新工作流参数验证中的字段名从key/value改为Key/DisplayValue
移除未使用的merge_workflow_with_parameters方法
添加get_station_info方法获取工作站基础信息
清理实验文件中的注释代码和更新导入路径

* fix: 修复资源移除时的父资源检查问题

在BaseROS2DeviceNode中,移除资源前添加对父资源是否为None的检查,避免空指针异常
同时更新Bottle和BottleCarrier类以支持**kwargs参数
修正测试文件中Liquid_feeding_beaker的大小写拼写错误

* correct return message

---------

Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
2025-10-17 03:08:15 +08:00
Xuwznln
0260cbbedb Close #107
Update doc url.
2025-10-16 17:26:45 +08:00
Xuwznln
7c440d10ab Fix/resource UUID and doc fix (#109)
* Fix ResourceTreeSet load error

* Raise error when using unsupported type to create ResourceTreeSet

* Fix children key error

* Fix children key error

* Fix workstation resource not tracking

* Fix workstation deck & children resource dupe

* Fix workstation deck & children resource dupe

* Fix multiple resource error

* Fix resource tree update

* Fix resource tree update

* Force confirm uuid

* Tip more error log

* Refactor Bioyond workstation and experiment workflow (#105)

Refactored the Bioyond workstation classes to improve parameter handling and workflow management. Updated experiment.py to use BioyondReactionStation with deck and material mappings, and enhanced workflow step parameter mapping and execution logic. Adjusted JSON experiment configs, improved workflow sequence handling, and added UUID assignment to PLR materials. Removed unused station_config and material cache logic, and added detailed docstrings and debug output for workflow methods.

* Fix resource get.
Fix resource parent not found.
Mapping uuid for all resources.

* mount parent uuid

* Add logging configuration based on BasicConfig in main function

* fix workstation node error

* fix workstation node error

* Update boot example

* temp fix for resource get

* temp fix for resource get

* provide error info when cant find plr type

* pack repo info

* fix to plr type error

* fix to plr type error

* Update regular container method

* support no size init

* fix comprehensive_station.json

* fix comprehensive_station.json

* fix type conversion

* fix state loading for regular container

* Update deploy-docs.yml

* Update deploy-docs.yml

---------

Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
2025-10-16 17:26:07 +08:00
Xuwznln
c85c49817d Fix workstation startup
Update registry
2025-10-13 15:06:30 +08:00
Xuwznln
c70eafa5f0 Fix one-key installation build for windows 2025-10-13 15:06:29 +08:00
Junhan Chang
b64466d443 modify default config 2025-10-13 15:06:26 +08:00
Junhan Chang
ef3f24ed48 add plr_to_bioyond, and refactor bioyond stations 2025-10-13 15:06:25 +08:00
Xuwznln
2a8e8d014b Fix conda pack on windows 2025-10-13 13:19:45 +08:00
Xuwznln
e0da1c7217 Fix one-key installation build
Install conda-pack before pack command

Add conda-pack to base when building one-key installer

Fix param error when using mamba run

Try fix one-key build on linux
2025-10-13 03:33:00 +08:00
hh.(SII)
51d3e61723 fix: rename schema field to resource_schema with serialization and validation aliases (#104)
Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
2025-10-13 03:24:20 +08:00
Xuwznln
6b5765bbf3 Complete all one key installation 2025-10-13 03:24:19 +08:00
Xuwznln
eb1f3fbe1c Try fix one-key build on linux 2025-10-13 02:10:05 +08:00
Xuwznln
fb93b1cd94 fix startup env check.
add auto install during one-key installation
2025-10-13 01:59:53 +08:00
Xuwznln
9aeffebde1 0.10.7 Update (#101)
* Cleanup registry to be easy-understanding (#76)

* delete deprecated mock devices

* rename categories

* combine chromatographic devices

* rename rviz simulation nodes

* organic virtual devices

* parse vessel_id

* run registry completion before merge

---------

Co-authored-by: Xuwznln <18435084+Xuwznln@users.noreply.github.com>

* fix: workstation handlers and vessel_id parsing

* fix: working dir error when input config path
feat: report publish topic when error

* modify default discovery_interval to 15s

* feat: add trace log level

* feat: 添加ChinWe设备控制类,支持串口通信和电机控制功能 (#79)

* fix: drop_tips not using auto resource select

* fix: discard_tips error

* fix: discard_tips

* fix: prcxi_res

* add: prcxi res
fix: startup slow

* feat: workstation example

* fix pumps and liquid_handler handle

* feat: 优化protocol node节点运行日志

* fix all protocol_compilers and remove deprecated devices

* feat: 新增use_remote_resource参数

* fix and remove redundant info

* bugfixes on organic protocols

* fix filter protocol

* fix protocol node

* 临时兼容错误的driver写法

* fix: prcxi import error

* use call_async in all service to avoid deadlock

* fix: figure_resource

* Update recipe.yaml

* add workstation template and battery example

* feat: add sk & ak

* update workstation base

* Create workstation_architecture.md

* refactor: workstation_base 重构为仅含业务逻辑,通信和子设备管理交给 ProtocolNode

* refactor: ProtocolNode→WorkstationNode

* Add:msgs.action (#83)

* update: Workstation dev 将版本号从 0.10.3 更新为 0.10.4 (#84)

* Add:msgs.action

* update: 将版本号从 0.10.3 更新为 0.10.4

* simplify resource system

* uncompleted refactor

* example for use WorkstationBase

* feat: websocket

* feat: websocket test

* feat: workstation example

* feat: action status

* fix: station自己的方法注册错误

* fix: 还原protocol node处理方法

* fix: build

* fix: missing job_id key

* ws test version 1

* ws test version 2

* ws protocol

* 增加物料关系上传日志

* 增加物料关系上传日志

* 修正物料关系上传

* 修复工站的tracker实例追踪失效问题

* 增加handle检测,增加material edge关系上传

* 修复event loop错误

* 修复edge上报错误

* 修复async错误

* 更新schema的title字段

* 主机节点信息等支持自动刷新

* 注册表编辑器

* 修复status密集发送时,消息出错

* 增加addr参数

* fix: addr param

* fix: addr param

* 取消labid 和 强制config输入

* Add action definitions for LiquidHandlerSetGroup and LiquidHandlerTransferGroup

- Created LiquidHandlerSetGroup.action with fields for group name, wells, and volumes.
- Created LiquidHandlerTransferGroup.action with fields for source and target group names and unit volume.
- Both actions include response fields for return information and success status.

* Add LiquidHandlerSetGroup and LiquidHandlerTransferGroup actions to CMakeLists

* Add set_group and transfer_group methods to PRCXI9300Handler and update liquid_handler.yaml

* result_info改为字典类型

* 新增uat的地址替换

* runze multiple pump support

(cherry picked from commit 49354fcf39)

* remove runze multiple software obtainer

(cherry picked from commit 8bcc92a394)

* support multiple backbone

(cherry picked from commit 4771ff2347)

* Update runze pump format

* Correct runze multiple backbone

* Update runze_multiple_backbone

* Correct runze pump multiple receive method.

* Correct runze pump multiple receive method.

* 对于PRCXI9320的transfer_group,一对多和多对多

* 移除MQTT,更新launch文档,提供注册表示例文件,更新到0.10.5

* fix import error

* fix dupe upload registry

* refactor ws client

* add server timeout

* Fix: run-column with correct vessel id (#86)

* fix run_column

* Update run_column_protocol.py

(cherry picked from commit e5aa4d940a)

* resource_update use resource_add

* 新增版位推荐功能

* 重新规定了版位推荐的入参

* update registry with nested obj

* fix protocol node log_message, added create_resource return value

* fix protocol node log_message, added create_resource return value

* try fix add protocol

* fix resource_add

* 修复移液站错误的aspirate注册表

* Feature/xprbalance-zhida (#80)

* feat(devices): add Zhida GC/MS pretreatment automation workstation

* feat(devices): add mettler_toledo xpr balance

* balance

* 重新补全zhida注册表

* PRCXI9320 json

* PRCXI9320 json

* PRCXI9320 json

* fix resource download

* remove class for resource

* bump version to 0.10.6

* 更新所有注册表

* 修复protocolnode的兼容性

* 修复protocolnode的兼容性

* Update install md

* Add Defaultlayout

* 更新物料接口

* fix dict to tree/nested-dict converter

* coin_cell_station draft

* refactor: rename "station_resource" to "deck"

* add standardized BIOYOND resources: bottle_carrier, bottle

* refactor and add BIOYOND resources tests

* add BIOYOND deck assignment and pass all tests

* fix: update resource with correct structure; remove deprecated liquid_handler set_group action

* feat: 将新威电池测试系统驱动与配置文件并入 workstation_dev_YB2 (#92)

* feat: 新威电池测试系统驱动与注册文件

* feat: bring neware driver & battery.json into workstation_dev_YB2

* add bioyond studio draft

* bioyond station with communication init and resource sync

* fix bioyond station and registry

* fix: update resource with correct structure; remove deprecated liquid_handler set_group action

* frontend_docs

* create/update resources with POST/PUT for big amount/ small amount data

* create/update resources with POST/PUT for big amount/ small amount data

* refactor: add itemized_carrier instead of carrier consists of ResourceHolder

* create warehouse by factory func

* update bioyond launch json

* add child_size for itemized_carrier

* fix bioyond resource io

* Workstation templates: Resources and its CRUD, and workstation tasks (#95)

* coin_cell_station draft

* refactor: rename "station_resource" to "deck"

* add standardized BIOYOND resources: bottle_carrier, bottle

* refactor and add BIOYOND resources tests

* add BIOYOND deck assignment and pass all tests

* fix: update resource with correct structure; remove deprecated liquid_handler set_group action

* feat: 将新威电池测试系统驱动与配置文件并入 workstation_dev_YB2 (#92)

* feat: 新威电池测试系统驱动与注册文件

* feat: bring neware driver & battery.json into workstation_dev_YB2

* add bioyond studio draft

* bioyond station with communication init and resource sync

* fix bioyond station and registry

* create/update resources with POST/PUT for big amount/ small amount data

* refactor: add itemized_carrier instead of carrier consists of ResourceHolder

* create warehouse by factory func

* update bioyond launch json

* add child_size for itemized_carrier

* fix bioyond resource io

---------

Co-authored-by: h840473807 <47357934+h840473807@users.noreply.github.com>
Co-authored-by: Xie Qiming <97236197+Andy6M@users.noreply.github.com>

* 更新物料接口

* Workstation dev yb2 (#100)

* Refactor and extend reaction station action messages

* Refactor dispensing station tasks to enhance parameter clarity and add batch processing capabilities

- Updated `create_90_10_vial_feeding_task` to include detailed parameters for 90%/10% vial feeding, improving clarity and usability.
- Introduced `create_batch_90_10_vial_feeding_task` for batch processing of 90%/10% vial feeding tasks with JSON formatted input.
- Added `create_batch_diamine_solution_task` for batch preparation of diamine solution, also utilizing JSON formatted input.
- Refined `create_diamine_solution_task` to include additional parameters for better task configuration.
- Enhanced schema descriptions and default values for improved user guidance.

* 修复to_plr_resources

* add update remove

* 支持选择器注册表自动生成
支持转运物料

* 修复资源添加

* 修复transfer_resource_to_another生成

* 更新transfer_resource_to_another参数,支持spot入参

* 新增test_resource动作

* fix host_node error

* fix host_node test_resource error

* fix host_node test_resource error

* 过滤本地动作

* 移动内部action以兼容host node

* 修复同步任务报错不显示的bug

* feat: 允许返回非本节点物料,后面可以通过decoration进行区分,就不进行warning了

* update todo

* modify bioyond/plr converter, bioyond resource registry, and tests

* pass the tests

* update todo

* add conda-pack-build.yml

* add auto install script for conda-pack-build.yml

(cherry picked from commit 172599adcf)

* update conda-pack-build.yml

* update conda-pack-build.yml

* update conda-pack-build.yml

* update conda-pack-build.yml

* update conda-pack-build.yml

* Add version in __init__.py
Update conda-pack-build.yml
Add create_zip_archive.py

* Update conda-pack-build.yml

* Update conda-pack-build.yml (with mamba)

* Update conda-pack-build.yml

* Fix FileNotFoundError

* Try fix 'charmap' codec can't encode characters in position 16-23: character maps to <undefined>

* Fix unilabos msgs search error

* Fix environment_check.py

* Update recipe.yaml

* Update registry. Update uuid loop figure method. Update install docs.

* Fix nested conda pack

* Fix one-key installation path error

* Bump version to 0.10.7

* Workshop bj (#99)

* Add LaiYu Liquid device integration and tests

Introduce LaiYu Liquid device implementation, including backend, controllers, drivers, configuration, and resource files. Add hardware connection, tip pickup, and simplified test scripts, as well as experiment and registry configuration for LaiYu Liquid. Documentation and .gitignore for the device are also included.

* feat(LaiYu_Liquid): 重构设备模块结构并添加硬件文档

refactor: 重新组织LaiYu_Liquid模块目录结构
docs: 添加SOPA移液器和步进电机控制指令文档
fix: 修正设备配置中的最大体积默认值
test: 新增工作台配置测试用例
chore: 删除过时的测试脚本和配置文件

* add

* 重构: 将 LaiYu_Liquid.py 重命名为 laiyu_liquid_main.py 并更新所有导入引用

- 使用 git mv 将 LaiYu_Liquid.py 重命名为 laiyu_liquid_main.py
- 更新所有相关文件中的导入引用
- 保持代码功能不变,仅改善命名一致性
- 测试确认所有导入正常工作

* 修复: 在 core/__init__.py 中添加 LaiYuLiquidBackend 导出

- 添加 LaiYuLiquidBackend 到导入列表
- 添加 LaiYuLiquidBackend 到 __all__ 导出列表
- 确保所有主要类都可以正确导入

* 修复大小写文件夹名字

* 电池装配工站二次开发教程(带目录)上传至dev (#94)

* 电池装配工站二次开发教程

* Update intro.md

* 物料教程

* 更新物料教程,json格式注释

* Update prcxi driver & fix transfer_liquid mix_times (#90)

* Update prcxi driver & fix transfer_liquid mix_times

* fix: correct mix_times type

* Update liquid_handler registry

* test: prcxi.py

* Update registry from pr

* fix ony-key script not exist

* clean files

---------

Co-authored-by: Junhan Chang <changjh@dp.tech>
Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com>
Co-authored-by: Guangxin Zhang <guangxin.zhang.bio@gmail.com>
Co-authored-by: Xie Qiming <97236197+Andy6M@users.noreply.github.com>
Co-authored-by: h840473807 <47357934+h840473807@users.noreply.github.com>
Co-authored-by: LccLink <1951855008@qq.com>
Co-authored-by: lixinyu1011 <61094742+lixinyu1011@users.noreply.github.com>
Co-authored-by: shiyubo0410 <shiyubo@dp.tech>
2025-10-12 23:34:26 +08:00
102 changed files with 3619 additions and 39496 deletions

View File

@@ -3,7 +3,7 @@
package:
name: unilabos
version: 0.10.19
version: 0.11.1
source:
path: ../../unilabos
@@ -54,7 +54,7 @@ requirements:
- pymodbus
- matplotlib
- pylibftdi
- uni-lab::unilabos-env ==0.10.19
- uni-lab::unilabos-env ==0.11.1
about:
repository: https://github.com/deepmodeling/Uni-Lab-OS

View File

@@ -2,7 +2,7 @@
package:
name: unilabos-env
version: 0.10.19
version: 0.11.1
build:
noarch: generic

View File

@@ -3,7 +3,7 @@
package:
name: unilabos-full
version: 0.10.19
version: 0.11.1
build:
noarch: generic
@@ -11,7 +11,7 @@ build:
requirements:
run:
# Base unilabos package (includes unilabos-env)
- uni-lab::unilabos ==0.10.19
- uni-lab::unilabos ==0.11.1
# Documentation tools
- sphinx
- sphinx_rtd_theme

View File

@@ -27,14 +27,15 @@ python -c "import base64,sys; print('Authorization: Lab ' + base64.b64encode(f'{
### 2. --addr → BASE URL
| `--addr` 值 | BASE |
|-------------|------|
| `test` | `https://uni-lab.test.bohrium.com` |
| `uat` | `https://uni-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://uni-lab.bohrium.com` |
| `--addr` | BASE |
| ------------ | ----------------------------------- |
| `test` | `https://leap-lab.test.bohrium.com` |
| `uat` | `https://leap-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://leap-lab.bohrium.com` |
确认后设置:
```bash
BASE="<根据 addr 确定的 URL>"
AUTH="Authorization: Lab <gen_auth.py 输出的 token>"
@@ -65,7 +66,7 @@ curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"
返回:
```json
{"code": 0, "data": {"uuid": "xxx", "name": "实验室名称"}}
{ "code": 0, "data": { "uuid": "xxx", "name": "实验室名称" } }
```
记住 `data.uuid``lab_uuid`
@@ -90,6 +91,7 @@ curl -s -X POST "$BASE/api/v1/lab/reagent" \
```
返回成功时包含试剂 UUID
```json
{"code": 0, "data": {"uuid": "xxx", ...}}
```
@@ -98,28 +100,28 @@ curl -s -X POST "$BASE/api/v1/lab/reagent" \
## 试剂字段说明
| 字段 | 类型 | 必填 | 说明 | 示例 |
|------|------|------|------|------|
| `lab_uuid` | string | 是 | 实验室 UUID从 API #1 获取) | `"8511c672-..."` |
| `cas` | string | 是 | CAS 注册号 | `"7732-18-3"` |
| `name` | string | 是 | 试剂中文/英文名称 | `"水"` |
| `molecular_formula` | string | 是 | 分子式 | `"H2O"` |
| `smiles` | string | 是 | SMILES 表示 | `"O"` |
| `stock_in_quantity` | number | 是 | 入库数量 | `10` |
| `unit` | string | 是 | 单位(字符串,见下表) | `"mL"` |
| `supplier` | string | 否 | 供应商名称 | `"国药集团"` |
| `production_date` | string | 否 | 生产日期ISO 8601 | `"2025-11-18T00:00:00Z"` |
| `expiry_date` | string | 否 | 过期日期ISO 8601 | `"2026-11-18T00:00:00Z"` |
| 字段 | 类型 | 必填 | 说明 | 示例 |
| ------------------- | ------ | ---- | ----------------------------- | ------------------------ |
| `lab_uuid` | string | 是 | 实验室 UUID从 API #1 获取) | `"8511c672-..."` |
| `cas` | string | 是 | CAS 注册号 | `"7732-18-3"` |
| `name` | string | 是 | 试剂中文/英文名称 | `"水"` |
| `molecular_formula` | string | 是 | 分子式 | `"H2O"` |
| `smiles` | string | 是 | SMILES 表示 | `"O"` |
| `stock_in_quantity` | number | 是 | 入库数量 | `10` |
| `unit` | string | 是 | 单位(字符串,见下表) | `"mL"` |
| `supplier` | string | 否 | 供应商名称 | `"国药集团"` |
| `production_date` | string | 否 | 生产日期ISO 8601 | `"2025-11-18T00:00:00Z"` |
| `expiry_date` | string | 否 | 过期日期ISO 8601 | `"2026-11-18T00:00:00Z"` |
### unit 单位值
| 值 | 单位 |
|------|------|
| 值 | 单位 |
| ------ | ---- |
| `"mL"` | 毫升 |
| `"L"` | 升 |
| `"g"` | 克 |
| `"L"` | 升 |
| `"g"` | 克 |
| `"kg"` | 千克 |
| `"瓶"` | 瓶 |
| `"瓶"` | 瓶 |
> 根据试剂状态选择:液体用 `"mL"` / `"L"`,固体用 `"g"` / `"kg"`。
@@ -133,8 +135,22 @@ curl -s -X POST "$BASE/api/v1/lab/reagent" \
```json
[
{"cas": "7732-18-3", "name": "水", "molecular_formula": "H2O", "smiles": "O", "stock_in_quantity": 10, "unit": "mL"},
{"cas": "64-17-5", "name": "乙醇", "molecular_formula": "C2H6O", "smiles": "CCO", "stock_in_quantity": 5, "unit": "L"}
{
"cas": "7732-18-3",
"name": "水",
"molecular_formula": "H2O",
"smiles": "O",
"stock_in_quantity": 10,
"unit": "mL"
},
{
"cas": "64-17-5",
"name": "乙醇",
"molecular_formula": "C2H6O",
"smiles": "CCO",
"stock_in_quantity": 5,
"unit": "L"
}
]
```
@@ -160,9 +176,20 @@ cas,name,molecular_formula,smiles,stock_in_quantity,unit,supplier,production_dat
7732-18-3,水,H2O,O,10,mL,农夫山泉,2025-11-18T00:00:00Z,2026-11-18T00:00:00Z
```
### 日期格式规则(重要)
所有日期字段(`production_date``expiry_date`**必须**使用 ISO 8601 完整格式:`YYYY-MM-DDTHH:MM:SSZ`
- 用户输入 `2025-03-01` → 转换为 `"2025-03-01T00:00:00Z"`
- 用户输入 `2025/9/1` → 转换为 `"2025-09-01T00:00:00Z"`
- 用户未提供日期 → 使用当天日期 + `T00:00:00Z`,有效期默认 +1 年
**禁止**发送不带时间部分的日期字符串(如 `"2025-03-01"`API 会拒绝。
### 执行与汇报
每次 API 调用后:
1. 检查返回 `code`0 = 成功)
2. 记录成功/失败数量
3. 全部完成后汇总:「共录入 N 条试剂,成功 X 条,失败 Y 条」
@@ -172,28 +199,29 @@ cas,name,molecular_formula,smiles,stock_in_quantity,unit,supplier,production_dat
## 常见试剂速查表
| 名称 | CAS | 分子式 | SMILES |
|------|-----|--------|--------|
| 水 | 7732-18-3 | H2O | O |
| 乙醇 | 64-17-5 | C2H6O | CCO |
| 甲醇 | 67-56-1 | CH4O | CO |
| 丙酮 | 67-64-1 | C3H6O | CC(C)=O |
| 二甲基亚砜(DMSO) | 67-68-5 | C2H6OS | CS(C)=O |
| 乙酸乙酯 | 141-78-6 | C4H8O2 | CCOC(C)=O |
| 二氯甲烷 | 75-09-2 | CH2Cl2 | ClCCl |
| 四氢呋喃(THF) | 109-99-9 | C4H8O | C1CCOC1 |
| N,N-二甲基甲酰胺(DMF) | 68-12-2 | C3H7NO | CN(C)C=O |
| 氯仿 | 67-66-3 | CHCl3 | ClC(Cl)Cl |
| 乙腈 | 75-05-8 | C2H3N | CC#N |
| 甲苯 | 108-88-3 | C7H8 | Cc1ccccc1 |
| 正己烷 | 110-54-3 | C6H14 | CCCCCC |
| 异丙醇 | 67-63-0 | C3H8O | CC(C)O |
| 盐酸 | 7647-01-0 | HCl | Cl |
| 酸 | 7664-93-9 | H2SO4 | OS(O)(=O)=O |
| 氢氧化钠 | 1310-73-2 | NaOH | [Na]O |
| 碳酸钠 | 497-19-8 | Na2CO3 | [Na]OC([O-])=O.[Na+] |
| 氯化钠 | 7647-14-5 | NaCl | [Na]Cl |
| 乙二胺四乙酸(EDTA) | 60-00-4 | C10H16N2O8 | OC(=O)CN(CCN(CC(O)=O)CC(O)=O)CC(O)=O |
| 名称 | CAS | 分子式 | SMILES |
| --------------------- | --------- | ---------- | ------------------------------------ |
| 水 | 7732-18-3 | H2O | O |
| 乙醇 | 64-17-5 | C2H6O | CCO |
| 乙酸 | 64-19-7 | C2H4O2 | CC(O)=O |
| 甲醇 | 67-56-1 | CH4O | CO |
| 丙酮 | 67-64-1 | C3H6O | CC(C)=O |
| 二甲基亚砜(DMSO) | 67-68-5 | C2H6OS | CS(C)=O |
| 乙酸乙酯 | 141-78-6 | C4H8O2 | CCOC(C)=O |
| 二氯甲烷 | 75-09-2 | CH2Cl2 | ClCCl |
| 四氢呋喃(THF) | 109-99-9 | C4H8O | C1CCOC1 |
| N,N-二甲基甲酰胺(DMF) | 68-12-2 | C3H7NO | CN(C)C=O |
| 氯仿 | 67-66-3 | CHCl3 | ClC(Cl)Cl |
| 乙腈 | 75-05-8 | C2H3N | CC#N |
| 甲苯 | 108-88-3 | C7H8 | Cc1ccccc1 |
| 正己烷 | 110-54-3 | C6H14 | CCCCCC |
| 异丙醇 | 67-63-0 | C3H8O | CC(C)O |
| | 7647-01-0 | HCl | Cl |
| 硫酸 | 7664-93-9 | H2SO4 | OS(O)(=O)=O |
| 氢氧化钠 | 1310-73-2 | NaOH | [Na]O |
| 碳酸钠 | 497-19-8 | Na2CO3 | [Na]OC([O-])=O.[Na+] |
| 氯化钠 | 7647-14-5 | NaCl | [Na]Cl |
| 乙二胺四乙酸(EDTA) | 60-00-4 | C10H16N2O8 | OC(=O)CN(CCN(CC(O)=O)CC(O)=O)CC(O)=O |
> 此表仅供快速参考。对于不在表中的试剂agent 应根据化学知识推断或提示用户补充。

View File

@@ -1,11 +1,13 @@
---
name: batch-submit-experiment
description: Batch submit experiments (notebooks) to Uni-Lab platform — list workflows, generate node_params from registry schemas, submit multiple rounds, check notebook status. Use when the user wants to submit experiments, create notebooks, batch run workflows, check experiment status, or mentions 提交实验/批量实验/notebook/实验轮次/实验状态.
description: Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate node_params from registry schemas, submit multiple rounds, check notebook status. Use when the user wants to submit experiments, create notebooks, batch run workflows, check experiment status, or mentions 提交实验/批量实验/notebook/实验轮次/实验状态.
---
# 批量提交实验指南
# Uni-Lab 批量提交实验指南
通过云端 API 批量提交实验notebook支持多轮实验参数配置。根据 workflow 模板详情和本地设备注册表自动生成 `node_params` 模板。
通过 Uni-Lab 云端 API 批量提交实验notebook支持多轮实验参数配置。根据 workflow 模板详情和本地设备注册表自动生成 `node_params` 模板。
> **重要**:本指南中的 `Authorization: Lab <token>` 是 **Uni-Lab 平台专用的认证方式**`Lab` 是 Uni-Lab 的 auth scheme 关键字,**不是** HTTP Basic 认证。请勿将其替换为 `Basic`。
## 前置条件(缺一不可)
@@ -18,25 +20,28 @@ description: Batch submit experiments (notebooks) to Uni-Lab platform — list w
生成 AUTH token任选一种方式
```bash
# 方式一Python 一行生成
# 方式一Python 一行生成注意scheme 是 "Lab" 不是 "Basic"
python -c "import base64,sys; print('Authorization: Lab ' + base64.b64encode(f'{sys.argv[1]}:{sys.argv[2]}'.encode()).decode())" <ak> <sk>
# 方式二:手动计算
# base64(ak:sk) → Authorization: Lab <token>
# ⚠️ 这里的 "Lab" 是 Uni-Lab 平台的 auth scheme绝对不能用 "Basic" 替代
```
### 2. --addr → BASE URL
| `--addr` 值 | BASE |
|-------------|------|
| `test` | `https://uni-lab.test.bohrium.com` |
| `uat` | `https://uni-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://uni-lab.bohrium.com` |
| `--addr` | BASE |
| ------------ | ----------------------------------- |
| `test` | `https://leap-lab.test.bohrium.com` |
| `uat` | `https://leap-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://leap-lab.bohrium.com` |
确认后设置:
```bash
BASE="<根据 addr 确定的 URL>"
# ⚠️ Auth scheme 必须是 "Lab"Uni-Lab 专用),不是 "Basic"
AUTH="Authorization: Lab <上面命令输出的 token>"
```
@@ -44,18 +49,19 @@ AUTH="Authorization: Lab <上面命令输出的 token>"
**批量提交实验时需要本地注册表来解析 workflow 节点的参数 schema。**
按优先级搜索
**必须先用 Glob 工具搜索文件**,不要直接猜测路径
```
<workspace 根目录>/unilabos_data/req_device_registry_upload.json
<workspace 根目录>/req_device_registry_upload.json
Glob: **/req_device_registry_upload.json
```
也可直接 Glob 搜索:`**/req_device_registry_upload.json`
常见位置(仅供参考,以 Glob 实际结果为准):
- `<workspace>/unilabos_data/req_device_registry_upload.json`
- `<workspace>/req_device_registry_upload.json`
找到后**检查文件修改时间**并告知用户。超过 1 天提醒用户是否需要重新启动 `unilab`
**如果文件不存在** → 告知用户先运行 `unilab` 启动命令,等注册表生成后再执行。可跳过此步,但将无法自动生成参数模板,需要用户手动填写 `param`
**如果 Glob 搜索无结果** → 告知用户先运行 `unilab` 启动命令,等注册表生成后再执行。可跳过此步,但将无法自动生成参数模板,需要用户手动填写 `param`
### 4. workflow_uuid目标工作流
@@ -93,7 +99,7 @@ curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"
返回:
```json
{"code": 0, "data": {"uuid": "xxx", "name": "实验室名称"}}
{ "code": 0, "data": { "uuid": "xxx", "name": "实验室名称" } }
```
记住 `data.uuid``lab_uuid`
@@ -104,9 +110,33 @@ curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"
curl -s -X GET "$BASE/api/v1/lab/project/list?lab_uuid=$lab_uuid" -H "$AUTH"
```
返回项目列表,展示给用户选择。列出每个项目的 `uuid``name`
返回
用户**必须**选择一个项目,记住 `project_uuid`,后续创建 notebook 时需要提供。
```json
{
"code": 0,
"data": {
"items": [
{
"uuid": "1b3f249a-...",
"name": "bt",
"description": null,
"status": "active",
"created_at": "2026-04-09T14:31:28+08:00"
},
{
"uuid": "b6366243-...",
"name": "default",
"description": "默认项目",
"status": "active",
"created_at": "2026-03-26T11:13:36+08:00"
}
]
}
}
```
展示 `data.items[]` 中每个项目的 `name``uuid`,让用户选择。用户**必须**选择一个项目,记住 `project_uuid`(即选中项目的 `uuid`),后续创建 notebook 时需要提供。
### 3. 列出可用 workflow
@@ -123,6 +153,7 @@ curl -s -X GET "$BASE/api/v1/lab/workflow/template/detail/$workflow_uuid" -H "$A
```
返回 workflow 的完整结构,包含所有 action 节点信息。需要从响应中提取:
- 每个 action 节点的 `node_uuid`
- 每个节点对应的设备 ID`resource_template_name`
- 每个节点的动作名(`node_template_name`
@@ -142,30 +173,30 @@ curl -s -X POST "$BASE/api/v1/lab/notebook" \
```json
{
"lab_uuid": "<lab_uuid>",
"project_uuid": "<project_uuid>",
"workflow_uuid": "<workflow_uuid>",
"name": "<实验名称>",
"node_params": [
"lab_uuid": "<lab_uuid>",
"project_uuid": "<project_uuid>",
"workflow_uuid": "<workflow_uuid>",
"name": "<实验名称>",
"node_params": [
{
"sample_uuids": ["<样品UUID1>", "<样品UUID2>"],
"datas": [
{
"sample_uuids": ["<样品UUID1>", "<样品UUID2>"],
"datas": [
{
"node_uuid": "<workflow中的节点UUID>",
"param": {},
"sample_params": [
{
"container_uuid": "<容器UUID>",
"sample_value": {
"liquid_names": "<液体名称>",
"volumes": 1000
}
}
]
}
]
"node_uuid": "<workflow中的节点UUID>",
"param": {},
"sample_params": [
{
"container_uuid": "<容器UUID>",
"sample_value": {
"liquid_names": "<液体名称>",
"volumes": 1000
}
}
]
}
]
]
}
]
}
```
@@ -194,25 +225,25 @@ curl -s -X GET "$BASE/api/v1/lab/notebook/status?uuid=$notebook_uuid" -H "$AUTH"
### 每轮的字段
| 字段 | 类型 | 说明 |
|------|------|------|
| 字段 | 类型 | 说明 |
| -------------- | ------------- | ----------------------------------------- |
| `sample_uuids` | array\<uuid\> | 该轮实验的样品 UUID 数组,无样品时传 `[]` |
| `datas` | array | 该轮中每个 workflow 节点的参数配置 |
| `datas` | array | 该轮中每个 workflow 节点的参数配置 |
### datas 中每个节点
| 字段 | 类型 | 说明 |
|------|------|------|
| `node_uuid` | string | workflow 模板中的节点 UUID从 API #4 获取) |
| `param` | object | 动作参数(根据本地注册表 schema 填写) |
| `sample_params` | array | 样品相关参数(液体名、体积等) |
| 字段 | 类型 | 说明 |
| --------------- | ------ | -------------------------------------------- |
| `node_uuid` | string | workflow 模板中的节点 UUID从 API #4 获取) |
| `param` | object | 动作参数(根据本地注册表 schema 填写) |
| `sample_params` | array | 样品相关参数(液体名、体积等) |
### sample_params 中每条
| 字段 | 类型 | 说明 |
|------|------|------|
| `container_uuid` | string | 容器 UUID |
| `sample_value` | object | 样品值,如 `{"liquid_names": "水", "volumes": 1000}` |
| 字段 | 类型 | 说明 |
| ---------------- | ------ | ---------------------------------------------------- |
| `container_uuid` | string | 容器 UUID |
| `sample_value` | object | 样品值,如 `{"liquid_names": "水", "volumes": 1000}` |
---
@@ -233,6 +264,7 @@ python scripts/gen_notebook_params.py \
> 脚本位于本文档同级目录下的 `scripts/gen_notebook_params.py`。
脚本会:
1. 调用 workflow detail API 获取所有 action 节点
2. 读取本地注册表,为每个节点查找对应的 action schema
3. 生成 `notebook_template.json`,包含:
@@ -270,8 +302,11 @@ python scripts/gen_notebook_params.py \
"properties": {
"goal": {
"properties": {
"asp_vols": {"type": "array", "items": {"type": "number"}},
"sources": {"type": "array"}
"asp_vols": {
"type": "array",
"items": { "type": "number" }
},
"sources": { "type": "array" }
},
"required": ["asp_vols", "sources"]
}

View File

@@ -7,7 +7,7 @@
选项:
--auth <token> Lab tokenbase64(ak:sk) 的结果,不含 "Lab " 前缀)
--base <url> API 基础 URL如 https://uni-lab.test.bohrium.com
--base <url> API 基础 URL如 https://leap-lab.test.bohrium.com
--workflow-uuid <uuid> 目标 workflow 的 UUID
--registry <path> 本地注册表文件路径(默认自动搜索)
--rounds <n> 实验轮次数(默认 1
@@ -17,7 +17,7 @@
示例:
python gen_notebook_params.py \\
--auth YTFmZDlkNGUtxxxx \\
--base https://uni-lab.test.bohrium.com \\
--base https://leap-lab.test.bohrium.com \\
--workflow-uuid abc-123-def \\
--rounds 2
"""

View File

@@ -40,13 +40,13 @@ python ./scripts/gen_auth.py --config <config.py>
决定 API 请求发往哪个服务器。从启动命令的 `--addr` 参数获取:
| `--addr` 值 | BASE URL |
|-------------|----------|
| `test` | `https://uni-lab.test.bohrium.com` |
| `uat` | `https://uni-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://uni-lab.bohrium.com` |
| 其他自定义 URL | 直接使用该 URL |
| `--addr` | BASE URL |
| -------------- | ----------------------------------- |
| `test` | `https://leap-lab.test.bohrium.com` |
| `uat` | `https://leap-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://leap-lab.bohrium.com` |
| 其他自定义 URL | 直接使用该 URL |
#### 必备项 ③req_device_registry_upload.json设备注册表
@@ -54,11 +54,11 @@ python ./scripts/gen_auth.py --config <config.py>
**推断 working_dir**(即 `unilabos_data` 所在目录):
| 条件 | working_dir 取值 |
|------|------------------|
| 条件 | working_dir 取值 |
| -------------------- | -------------------------------------------------------- |
| 传了 `--working_dir` | `<working_dir>/unilabos_data/`(若子目录已存在则直接用) |
| 仅传了 `--config` | `<config 文件所在目录>/unilabos_data/` |
| 都没传 | `<当前工作目录>/unilabos_data/` |
| 仅传了 `--config` | `<config 文件所在目录>/unilabos_data/` |
| 都没传 | `<当前工作目录>/unilabos_data/` |
**按优先级搜索文件**
@@ -84,24 +84,6 @@ python ./scripts/gen_auth.py --config <config.py>
python ./scripts/extract_device_actions.py --registry <找到的文件路径>
```
#### 完整示例
用户提供:
```
--ak a1fd9d4e-xxxx-xxxx-xxxx-d9a69c09f0fd
--sk 136ff5c6-xxxx-xxxx-xxxx-a03e301f827b
--addr test
--port 8003
--disable_browser
```
从中提取:
- ✅ ak/sk → 运行 `gen_auth.py` 得到 `AUTH="Authorization: Lab YTFmZDlk..."`
- ✅ addr=test → `BASE=https://uni-lab.test.bohrium.com`
- ✅ 搜索 `unilabos_data/req_device_registry_upload.json` → 找到并确认时间
- ✅ 用户指明目标设备 → 如 `liquid_handler.prcxi`
**四项全部就绪后才进入 Step 1。**
### Step 1 — 列出可用设备
@@ -129,6 +111,7 @@ python ./scripts/extract_device_actions.py [--registry <path>] <device_id> ./ski
脚本会显示设备的 Python 源码路径和类名,方便阅读源码了解参数含义。
每个 action 生成一个 JSON 文件,包含:
- `type` — 作为 API 调用的 `action_type`
- `schema` — 完整 JSON Schema`properties.goal.properties` 参数定义)
- `goal` — goal 字段映射(含占位符 `$placeholder`
@@ -136,13 +119,14 @@ python ./scripts/extract_device_actions.py [--registry <path>] <device_id> ./ski
### Step 3 — 写 action-index.md
按模板为每个 action 写条目:
按模板为每个 action 写条目**必须包含 `action_type`**
```markdown
### `<action_name>`
<用途描述(一句话)>
- **action_type**: `<从 actions/<name>.json 的 type 字段获取>`
- **Schema**: [`actions/<filename>.json`](actions/<filename>.json)
- **核心参数**: `param1`, `param2`(从 schema.required 获取)
- **可选参数**: `param3`, `param4`
@@ -150,6 +134,8 @@ python ./scripts/extract_device_actions.py [--registry <path>] <device_id> ./ski
```
描述规则:
- **每个 action 必须标注 `action_type`**(从 JSON 的 `type` 字段读取),这是 API #9 调用时的必填参数,传错会导致任务永远卡住
-`schema.properties` 读参数列表schema 已提升为 goal 内容)
-`schema.required` 区分核心/可选参数
- 按功能分类(移液、枪头、外设等)
@@ -165,6 +151,7 @@ python ./scripts/extract_device_actions.py [--registry <path>] <device_id> ./ski
### Step 4 — 写 SKILL.md
直接复用 `unilab-device-api` 的 API 模板,修改:
- 设备名称
- Action 数量
- 目录列表
@@ -172,42 +159,77 @@ python ./scripts/extract_device_actions.py [--registry <path>] <device_id> ./ski
- **AUTH 头** — 使用 Step 0 中 `gen_auth.py` 生成的 `Authorization: Lab <token>`(不要硬编码 `Api` 类型的 key
- **Python 源码路径** — 在 SKILL.md 开头注明设备对应的源码文件,方便参考参数含义
- **Slot 字段表** — 列出本设备哪些 action 的哪些字段需要填入 Slot物料/设备/节点/类名)
- **action_type 速查表** — 在 API #9 说明后面紧跟一个表格,列出每个 action 对应的 `action_type` 值(从 JSON `type` 字段提取),方便 agent 快速查找而无需打开 JSON 文件
API 模板结构:
```markdown
## 设备信息
- device_id, Python 源码路径, 设备类名
## 前置条件(缺一不可)
- ak/sk → AUTH, --addr → BASE URL
## 请求约定
- Windows 平台必须用 curl.exe非 PowerShell 的 curl 别名)
## Session State
- lab_uuid通过 GET /edge/lab/info 直接获取,不要问用户), device_name
## API Endpoints
# - #1 GET /edge/lab/info → 直接拿到 lab_uuid
# - #2 创建工作流 POST /lab/workflow/owner → 拼 URL 告知用户
# - #3 创建节点 POST /edge/workflow/node
# body: {workflow_uuid, resource_template_name: "<device_id>", node_template_name: "<action_name>"}
# - #4 删除节点 DELETE /lab/workflow/nodes
# - #5 更新节点参数 PATCH /lab/workflow/node
# - #6 查询节点 handles POST /lab/workflow/node-handles
# body: {node_uuids: ["uuid1","uuid2"]} → 返回各节点的 handle_uuid
# - #7 批量创建边 POST /lab/workflow/edges
# body: {edges: [{source_node_uuid, target_node_uuid, source_handle_uuid, target_handle_uuid}]}
# - #8 启动工作流 POST /lab/workflow/{uuid}/run
# - #9 运行设备单动作 POST /lab/mcp/run/action
# - #1 GET /edge/lab/info → 直接拿到 lab_uuid
# - #2 创建工作流 POST /lab/workflow/owner → 拼 URL 告知用户
# - #3 创建节点 POST /edge/workflow/node
# body: {workflow_uuid, resource_template_name: "<device_id>", node_template_name: "<action_name>"}
# - #4 删除节点 DELETE /lab/workflow/nodes
# - #5 更新节点参数 PATCH /lab/workflow/node
# - #6 查询节点 handles POST /lab/workflow/node-handles
# body: {node_uuids: ["uuid1","uuid2"]} → 返回各节点的 handle_uuid
# - #7 批量创建边 POST /lab/workflow/edges
# body: {edges: [{source_node_uuid, target_node_uuid, source_handle_uuid, target_handle_uuid}]}
# - #8 启动工作流 POST /lab/workflow/{uuid}/run
# - #9 运行设备单动作 POST /lab/mcp/run/action action_type 必须从 action-index.md 或 actions/<name>.json 的 type 字段获取,传错会导致任务永远卡住)
# - #10 查询任务状态 GET /lab/mcp/task/{task_uuid}
# - #11 运行工作流单节点 POST /lab/mcp/run/workflow/action
# - #12 获取资源树 GET /lab/material/download/{lab_uuid}
# - #13 获取工作流模板详情 GET /lab/workflow/template/detail/{workflow_uuid}
# 返回 workflow 完整结构data.nodes[] 含每个节点的 uuid、name、param、device_name、handles
# 返回 workflow 完整结构data.nodes[] 含每个节点的 uuid、name、param、device_name、handles
# - #14 按名称查询物料模板 GET /lab/material/template/by-name?lab_uuid=&name=
# 返回 res_template_uuid用于 #15 创建物料时的必填字段
# - #15 创建物料节点 POST /edge/material/node
# body: {res_template_uuid(从#14获取), name(自定义), display_name, parent_uuid?(从#12获取), ...}
# - #16 更新物料节点 PUT /edge/material/node
# body: {uuid(从#12获取), display_name?, description?, init_param_data?, data?, ...}
## Placeholder Slot 填写规则
- unilabos_resources → ResourceSlot → {"id":"/path/name","name":"name","uuid":"xxx"}
- unilabos_devices → DeviceSlot → "/parent/device" 路径字符串
- unilabos_nodes → NodeSlot → "/parent/node" 路径字符串
@@ -217,13 +239,15 @@ API 模板结构:
- 列出本设备所有 Slot 字段、类型及含义
## 渐进加载策略
## 完整工作流 Checklist
```
### Step 5 — 验证
检查文件完整性:
- [ ] `SKILL.md` 包含 API endpoint#1 获取 lab_uuid、#2-#7 工作流/节点/边、#8-#11 运行/查询、#12 资源树、#13 工作流模板详情)
- [ ] `SKILL.md` 包含 API endpoint#1 获取 lab_uuid、#2-#7 工作流/节点/边、#8-#11 运行/查询、#12 资源树、#13 工作流模板详情、#14-#16 物料管理)
- [ ] `SKILL.md` 包含 Placeholder Slot 填写规则ResourceSlot / DeviceSlot / NodeSlot / ClassSlot / FormulationSlot + create_resource 特例)和本设备的 Slot 字段表
- [ ] `action-index.md` 列出所有 action 并有描述
- [ ] `actions/` 目录中每个 action 有对应 JSON 文件
@@ -272,92 +296,48 @@ API 模板结构:
`placeholder_keys` / `_unilabos_placeholder_info` 中有 5 种值,对应不同的填写方式:
| placeholder 值 | Slot 类型 | 填写格式 | 选取范围 |
|---------------|-----------|---------|---------|
| `unilabos_resources` | ResourceSlot | `{"id": "/path/name", "name": "name", "uuid": "xxx"}` | 仅**物料**节点(不含设备) |
| `unilabos_devices` | DeviceSlot | `"/parent/device_name"` | 仅**设备**节点type=device路径字符串 |
| `unilabos_nodes` | NodeSlot | `"/parent/node_name"` | **设备 + 物料**,即所有节点,路径字符串 |
| `unilabos_class` | ClassSlot | `"class_name"` | 注册表中已上报的资源类 name |
| `unilabos_formulation` | FormulationSlot | `[{well_name, liquids: [{name, volume}]}]` | 资源树中物料节点的 **name**,配合液体配方 |
| placeholder 值 | Slot 类型 | 填写格式 | 选取范围 |
| ---------------------- | --------------- | ----------------------------------------------------- | ----------------------------------------- |
| `unilabos_resources` | ResourceSlot | `{"id": "/path/name", "name": "name", "uuid": "xxx"}` | 仅**物料**节点(不含设备) |
| `unilabos_devices` | DeviceSlot | `"/parent/device_name"` | 仅**设备**节点type=device路径字符串 |
| `unilabos_nodes` | NodeSlot | `"/parent/node_name"` | **设备 + 物料**,即所有节点,路径字符串 |
| `unilabos_class` | ClassSlot | `"class_name"` | 注册表中已上报的资源类 name |
| `unilabos_formulation` | FormulationSlot | `[{well_name, liquids: [{name, volume}]}]` | 资源树中物料节点的 **name**,配合液体配方 |
### ResourceSlot`unilabos_resources`
最常见的类型。从资源树中选取**物料**节点(孔板、枪头盒、试剂槽等):
```json
{"id": "/workstation/container1", "name": "container1", "uuid": "ff149a9a-2cb8-419d-8db5-d3ba056fb3c2"}
```
- 单个:`{"id": "/workstation/container1", "name": "container1", "uuid": "ff149a9a-..."}`
- 数组:`[{"id": "/path/a", "name": "a", "uuid": "xxx"}, ...]`
- `id` 从 parent 计算的路径格式,根据 action 语义选择正确的物料
- 单个schema type=object`{"id": "/path/name", "name": "name", "uuid": "xxx"}`
- 数组schema type=array`[{"id": "/path/a", "name": "a", "uuid": "xxx"}, ...]`
- `id` 本身是从 parent 计算的路径格式
- 根据 action 语义选择正确的物料(如 `sources` = 液体来源,`targets` = 目标位置)
> **特例**`create_resource` 的 `res_id`,目标物料可能尚不存在,直接填期望路径,不需要 uuid。
> **特例**`create_resource` 的 `res_id` 字段,目标物料可能**尚不存在**,此时直接填写期望的路径(如 `"/workstation/container1"`),不需要 uuid。
### DeviceSlot / NodeSlot / ClassSlot
### DeviceSlot`unilabos_devices`
填写**设备路径字符串**。从资源树中筛选 type=device 的节点,从 parent 计算路径:
```
"/host_node"
"/bioyond_cell/reaction_station"
```
- 只填路径字符串,不需要 `{id, uuid}` 对象
- 根据 action 语义选择正确的设备(如 `target_device_id` = 目标设备)
### NodeSlot`unilabos_nodes`
范围 = 设备 + 物料。即资源树中**所有节点**都可以选,填写**路径字符串**
```
"/PRCXI/PRCXI_Deck"
```
- 使用场景:当参数既可能指向物料也可能指向设备时(如 `PumpTransferProtocol``from_vessel`/`to_vessel``create_resource``parent`
### ClassSlot`unilabos_class`
填写注册表中已上报的**资源类 name**。从本地 `req_resource_registry_upload.json` 中查找:
```
"container"
```
- **DeviceSlot**`unilabos_devices`):路径字符串如 `"/host_node"`,仅 type=device 的节点
- **NodeSlot**`unilabos_nodes`):路径字符串如 `"/PRCXI/PRCXI_Deck"`,设备 + 物料均可选
- **ClassSlot**`unilabos_class`):类名字符串如 `"container"`,从 `req_resource_registry_upload.json` 查找
### FormulationSlot`unilabos_formulation`
描述**液体配方**:向哪些物料容器中加入哪些液体及体积。填写为**对象数组**
描述**液体配方**:向哪些容器中加入哪些液体及体积。
```json
[
{
"sample_uuid": "",
"well_name": "YB_PrepBottle_15mL_Carrier_bottle_A1",
"liquids": [
{ "name": "LiPF6", "volume": 0.6 },
{ "name": "DMC", "volume": 1.2 }
]
"well_name": "bottle_A1",
"liquids": [{ "name": "LiPF6", "volume": 0.6 }]
}
]
```
#### 字段说明
| 字段 | 类型 | 说明 |
|------|------|------|
| `sample_uuid` | string | 样品 UUID无样品时传空字符串 `""` |
| `well_name` | string | 目标物料容器的 **name**(从资源树中取物料节点的 `name` 字段,如瓶子、孔位名称) |
| `liquids` | array | 要加入的液体列表 |
| `liquids[].name` | string | 液体名称(如试剂名、溶剂名) |
| `liquids[].volume` | number | 液体体积(单位由设备决定,通常为 mL |
#### 填写规则
- `well_name` 必须是资源树中已存在的物料节点 `name`(不是 `id` 路径),通过 API #12 获取资源树后筛选
- 每个数组元素代表一个目标容器的配方
- 一个容器可以加入多种液体(`liquids` 数组多条记录)
- 与 ResourceSlot 的区别ResourceSlot 填 `{id, name, uuid}` 指向物料本身FormulationSlot 用 `well_name` 引用物料,并附带液体配方信息
- `well_name` — 目标物料的 **name**(从资源树取,不是 `id` 路径)
- `liquids[]` — 液体列表,每条含 `name`(试剂名)和 `volume`体积单位由上下文决定pylabrobot 内部统一 uL
- `sample_uuid` — 样品 UUID无样品传 `""`
- 与 ResourceSlot 的区别ResourceSlot 指向物料本身FormulationSlot 引用物料名并附带配方信息
### 通过 API #12 获取资源树
@@ -365,7 +345,147 @@ API 模板结构:
curl -s -X GET "$BASE/api/v1/lab/material/download/$lab_uuid" -H "$AUTH"
```
注意 `lab_uuid` 在路径中(不是查询参数)。资源树返回所有节点,每个节点包含 `id`(路径格式)、`name``uuid``type``parent` 等字段。填写 Slot 时需根据 placeholder 类型筛选正确的节点。
注意 `lab_uuid` 在路径中(不是查询参数)。返回结构:
```json
{
"code": 0,
"data": {
"nodes": [
{"name": "host_node", "uuid": "c3ec1e68-...", "type": "device", "parent": ""},
{"name": "PRCXI", "uuid": "e249c9a6-...", "type": "device", "parent": ""},
{"name": "PRCXI_Deck", "uuid": "fb6a8b71-...", "type": "deck", "parent": "PRCXI"}
],
"edges": [...]
}
}
```
- `data.nodes[]` — 所有节点(设备 + 物料),每个节点含 `name``uuid``type``parent`
- `type` 区分设备(`device`)和物料(`deck``container``resource` 等)
- `parent` 为父节点名称(空字符串表示顶级)
- 填写 Slot 时根据 placeholder 类型筛选ResourceSlot 取非 device 节点DeviceSlot 取 device 节点
- 创建/更新物料时:`parent_uuid` 取父节点的 `uuid`,更新目标的 `uuid` 取节点自身的 `uuid`
## 物料管理 API
设备 Skill 除了设备动作外,还需支持物料节点的创建和参数设定,用于在资源树中动态管理物料。
典型流程:先通过 **#14 按名称查询模板** 获取 `res_template_uuid` → 再通过 **#15 创建物料** → 之后可通过 **#16 更新物料** 修改属性。更新时需要的 `uuid``parent_uuid` 均从 **#12 资源树下载** 获取。
### API #14 — 按名称查询物料模板
创建物料前,需要先获取物料模板的 UUID。通过模板名称查询
```bash
curl -s -X GET "$BASE/api/v1/lab/material/template/by-name?lab_uuid=$lab_uuid&name=<template_name>" -H "$AUTH"
```
| 参数 | 必填 | 说明 |
| ---------- | ------ | -------------------------------- |
| `lab_uuid` | **是** | 实验室 UUID从 API #1 获取) |
| `name` | **是** | 物料模板名称(如 `"container"` |
返回 `code: 0` 时,**`data.uuid`** 即为 `res_template_uuid`,用于 API #15 创建物料。返回还包含 `name``resource_type``handles``config_infos` 等模板元信息。
模板不存在时返回 `code: 10002``data` 为空对象。模板名称来自资源注册表中已注册的资源类型。
### API #15 — 创建物料节点
```bash
curl -s -X POST "$BASE/api/v1/edge/material/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '<request_body>'
```
请求体:
```json
{
"res_template_uuid": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
"name": "my_custom_bottle",
"display_name": "自定义瓶子",
"parent_uuid": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
"type": "",
"init_param_data": {},
"schema": {},
"data": {
"liquids": [["water", 1000, "uL"]],
"max_volume": 50000
},
"plate_well_datas": {},
"plate_reagent_datas": {},
"pose": {},
"model": {}
}
```
| 字段 | 必填 | 类型 | 数据来源 | 说明 |
| --------------------- | ------ | ------------- | ----------------------------------- | -------------------------------------- |
| `res_template_uuid` | **是** | string (UUID) | **API #14** 按名称查询获取 | 物料模板 UUID |
| `name` | 否 | string | **用户自定义** | 节点名称(标识符),可自由命名 |
| `display_name` | 否 | string | 用户自定义 | 显示名称UI 展示用) |
| `parent_uuid` | 否 | string (UUID) | **API #12** 资源树中父节点的 `uuid` | 父节点,为空则创建顶级节点 |
| `type` | 否 | string | 从模板继承 | 节点类型 |
| `init_param_data` | 否 | object | 用户指定 | 初始化参数,覆盖模板默认值 |
| `data` | 否 | object | 用户指定 | 节点数据container 见下方 data 格式 |
| `plate_well_datas` | 否 | object | 用户指定 | 孔板子节点数据(创建带孔位的板时使用) |
| `plate_reagent_datas` | 否 | object | 用户指定 | 试剂关联数据 |
| `schema` | 否 | object | 从模板继承 | 自定义 schema不传则从模板继承 |
| `pose` | 否 | object | 用户指定 | 位姿信息 |
| `model` | 否 | object | 用户指定 | 3D 模型信息 |
#### container 的 `data` 格式
> **体积单位统一为 uL微升**。pylabrobot 体系中所有体积值(`max_volume`、`liquids` 中的 volume均为 uL。外部如果是 mL 需乘 1000 转换。
```json
{
"liquids": [["water", 1000, "uL"], ["ethanol", 500, "uL"]],
"max_volume": 50000
}
```
- `liquids` — 液体列表,每条为 `[液体名称, 体积(uL), 单位字符串]`
- `max_volume` — 容器最大容量uL如 50 mL = 50000 uL
### API #16 — 更新物料节点
```bash
curl -s -X PUT "$BASE/api/v1/edge/material/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '<request_body>'
```
请求体:
```json
{
"uuid": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
"parent_uuid": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
"display_name": "新显示名称",
"description": "新描述",
"init_param_data": {},
"data": {},
"pose": {},
"schema": {},
"extra": {}
}
```
| 字段 | 必填 | 类型 | 数据来源 | 说明 |
| ----------------- | ------ | ------------- | ------------------------------------- | ---------------- |
| `uuid` | **是** | string (UUID) | **API #12** 资源树中目标节点的 `uuid` | 要更新的物料节点 |
| `parent_uuid` | 否 | string (UUID) | API #12 资源树 | 移动到新父节点 |
| `display_name` | 否 | string | 用户指定 | 更新显示名称 |
| `description` | 否 | string | 用户指定 | 更新描述 |
| `init_param_data` | 否 | object | 用户指定 | 更新初始化参数 |
| `data` | 否 | object | 用户指定 | 更新节点数据 |
| `pose` | 否 | object | 用户指定 | 更新位姿 |
| `schema` | 否 | object | 用户指定 | 更新 schema |
| `extra` | 否 | object | 用户指定 | 更新扩展数据 |
> 只传需要更新的字段,未传的字段保持不变。
## 最终目录结构

View File

@@ -0,0 +1,251 @@
---
name: host-node
description: Operate Uni-Lab host node via REST API — create resources, test latency, test resource tree, manual confirm. Use when the user mentions host_node, creating resources, resource management, testing latency, or any host node operation.
---
# Host Node API Skill
## 设备信息
- **device_id**: `host_node`
- **Python 源码**: `unilabos/ros/nodes/presets/host_node.py`
- **设备类**: `HostNode`
- **动作数**: 4`create_resource`, `test_latency`, `auto-test_resource`, `manual_confirm`
## 前置条件(缺一不可)
使用本 skill 前,**必须**先确认以下信息。如果缺少任何一项,**立即向用户询问并终止**,等补齐后再继续。
### 1. ak / sk → AUTH
从启动参数 `--ak` `--sk` 或 config.py 中获取,生成 token`base64(ak:sk)``Authorization: Lab <token>`
### 2. --addr → BASE URL
| `--addr` 值 | BASE |
| ------------ | ----------------------------------- |
| `test` | `https://leap-lab.test.bohrium.com` |
| `uat` | `https://leap-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://leap-lab.bohrium.com` |
确认后设置:
```bash
BASE="<根据 addr 确定的 URL>"
AUTH="Authorization: Lab <token>"
```
**两项全部就绪后才可发起 API 请求。**
## Session State
在整个对话过程中agent 需要记住以下状态,避免重复询问用户:
- `lab_uuid` — 实验室 UUID首次通过 API #1 自动获取,**不需要问用户**
- `device_name``host_node`
## 请求约定
所有请求使用 `curl -s`POST/PATCH/DELETE 需加 `Content-Type: application/json`
> **Windows 平台**必须使用 `curl.exe`(而非 PowerShell 的 `curl` 别名)。
---
## API Endpoints
### 1. 获取实验室信息(自动获取 lab_uuid
```bash
curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"
```
返回 `data.uuid``lab_uuid``data.name``lab_name`
### 2. 创建工作流
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/owner" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"name":"<名称>","lab_uuid":"<lab_uuid>","description":"<描述>"}'
```
返回 `data.uuid``workflow_uuid`。创建成功后告知用户链接:`$BASE/laboratory/$lab_uuid/workflow/$workflow_uuid`
### 3. 创建节点
```bash
curl -s -X POST "$BASE/api/v1/edge/workflow/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"workflow_uuid":"<workflow_uuid>","resource_template_name":"host_node","node_template_name":"<action_name>"}'
```
- `resource_template_name` 固定为 `host_node`
- `node_template_name` — action 名称(如 `create_resource`, `test_latency`
### 4. 删除节点
```bash
curl -s -X DELETE "$BASE/api/v1/lab/workflow/nodes" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"node_uuids":["<uuid1>"],"workflow_uuid":"<workflow_uuid>"}'
```
### 5. 更新节点参数
```bash
curl -s -X PATCH "$BASE/api/v1/lab/workflow/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"workflow_uuid":"<wf_uuid>","uuid":"<node_uuid>","param":{...}}'
```
`param` 直接使用创建节点返回的 `data.param` 结构,修改需要填入的字段值。参考 [action-index.md](action-index.md) 确定哪些字段是 Slot。
### 6. 查询节点 handles
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/node-handles" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"node_uuids":["<node_uuid_1>","<node_uuid_2>"]}'
```
### 7. 批量创建边
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/edges" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"edges":[{"source_node_uuid":"<uuid>","target_node_uuid":"<uuid>","source_handle_uuid":"<uuid>","target_handle_uuid":"<uuid>"}]}'
```
### 8. 启动工作流
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/<workflow_uuid>/run" -H "$AUTH"
```
### 9. 运行设备单动作
```bash
curl -s -X POST "$BASE/api/v1/lab/mcp/run/action" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"lab_uuid":"<lab_uuid>","device_id":"host_node","action":"<action_name>","action_type":"<type>","param":{...}}'
```
`param` 直接放 goal 里的属性,**不要**再包一层 `{"goal": {...}}`
> **WARNING: `action_type` 必须正确,传错会导致任务永远卡住无法完成。** 从下表或 `actions/<name>.json` 的 `type` 字段获取。
#### action_type 速查表
| action | action_type |
|--------|-------------|
| `test_latency` | `UniLabJsonCommand` |
| `create_resource` | `ResourceCreateFromOuterEasy` |
| `auto-test_resource` | `UniLabJsonCommand` |
| `manual_confirm` | `UniLabJsonCommand` |
### 10. 查询任务状态
```bash
curl -s -X GET "$BASE/api/v1/lab/mcp/task/<task_uuid>" -H "$AUTH"
```
### 11. 运行工作流单节点
```bash
curl -s -X POST "$BASE/api/v1/lab/mcp/run/workflow/action" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"node_uuid":"<node_uuid>"}'
```
### 12. 获取资源树(物料信息)
```bash
curl -s -X GET "$BASE/api/v1/lab/material/download/$lab_uuid" -H "$AUTH"
```
注意 `lab_uuid` 在路径中。返回 `data.nodes[]` 含所有节点(设备 + 物料),每个节点含 `name``uuid``type``parent`
### 13. 获取工作流模板详情
```bash
curl -s -X GET "$BASE/api/v1/lab/workflow/template/detail/$workflow_uuid" -H "$AUTH"
```
> 必须使用 `/lab/workflow/template/detail/{uuid}`,其他路径会返回 404。
### 14. 按名称查询物料模板
```bash
curl -s -X GET "$BASE/api/v1/lab/material/template/by-name?lab_uuid=$lab_uuid&name=<template_name>" -H "$AUTH"
```
返回 `data.uuid``res_template_uuid`,用于 API #15
### 15. 创建物料节点
```bash
curl -s -X POST "$BASE/api/v1/edge/material/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"res_template_uuid":"<uuid>","name":"<名称>","display_name":"<显示名>","parent_uuid":"<父节点uuid>","data":{...}}'
```
### 16. 更新物料节点
```bash
curl -s -X PUT "$BASE/api/v1/edge/material/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"uuid":"<节点uuid>","display_name":"<新名称>","data":{...}}'
```
---
## Placeholder Slot 填写规则
| `placeholder_keys` 值 | Slot 类型 | 填写格式 | 选取范围 |
| --------------------- | ------------ | ----------------------------------------------------- | ---------------------- |
| `unilabos_resources` | ResourceSlot | `{"id": "/path/name", "name": "name", "uuid": "xxx"}` | 仅物料节点(非设备) |
| `unilabos_devices` | DeviceSlot | `"/parent/device_name"` | 仅设备节点type=device |
| `unilabos_nodes` | NodeSlot | `"/parent/node_name"` | 所有节点(设备 + 物料) |
| `unilabos_class` | ClassSlot | `"class_name"` | 注册表中已注册的资源类 |
### host_node 设备的 Slot 字段表
| Action | 字段 | Slot 类型 | 说明 |
| ----------------- | ----------- | ------------ | ------------------------------ |
| `create_resource` | `res_id` | ResourceSlot | 新资源路径(可填不存在的路径) |
| `create_resource` | `device_id` | DeviceSlot | 归属设备 |
| `create_resource` | `parent` | NodeSlot | 父节点路径 |
| `create_resource` | `class_name`| ClassSlot | 资源类名如 `"container"` |
| `auto-test_resource` | `resource` | ResourceSlot | 单个测试物料 |
| `auto-test_resource` | `resources` | ResourceSlot | 测试物料数组 |
| `auto-test_resource` | `device` | DeviceSlot | 测试设备 |
| `auto-test_resource` | `devices` | DeviceSlot | 测试设备 |
---
## 渐进加载策略
1. **SKILL.md**(本文件)— API 端点 + session state 管理
2. **[action-index.md](action-index.md)** — 按分类浏览 4 个动作的描述和核心参数
3. **[actions/\<name\>.json](actions/)** — 仅在需要构建具体请求时,加载对应 action 的完整 JSON Schema
---
## 完整工作流 Checklist
```
Task Progress:
- [ ] Step 1: GET /edge/lab/info 获取 lab_uuid
- [ ] Step 2: 获取资源树 (GET #12) → 记住可用物料
- [ ] Step 3: 读 action-index.md 确定要用的 action 名
- [ ] Step 4: 创建工作流 (POST #2) → 记住 workflow_uuid告知用户链接
- [ ] Step 5: 创建节点 (POST #3, resource_template_name=host_node) → 记住 node_uuid + data.param
- [ ] Step 6: 根据 _unilabos_placeholder_info 和资源树,填写 data.param 中的 Slot 字段
- [ ] Step 7: 更新节点参数 (PATCH #5)
- [ ] Step 8: 查询节点 handles (POST #6) → 获取各节点的 handle_uuid
- [ ] Step 9: 批量创建边 (POST #7) → 用 handle_uuid 连接节点
- [ ] Step 10: 启动工作流 (POST #8) 或运行单节点 (POST #11)
- [ ] Step 11: 查询任务状态 (GET #10) 确认完成
```

View File

@@ -0,0 +1,58 @@
# Action Index — host_node
4 个动作,按功能分类。每个动作的完整 JSON Schema 在 `actions/<name>.json`
---
## 资源管理
### `create_resource`
在资源树中创建新资源(容器、物料等),支持指定位置、类型和初始液体
- **action_type**: `ResourceCreateFromOuterEasy`
- **Schema**: [`actions/create_resource.json`](actions/create_resource.json)
- **可选参数**: `res_id`, `device_id`, `class_name`, `parent`, `bind_locations`, `liquid_input_slot`, `liquid_type`, `liquid_volume`, `slot_on_deck`
- **占位符字段**:
- `res_id`**ResourceSlot**(特例:目标物料可能尚不存在,直接填期望路径)
- `device_id`**DeviceSlot**,填路径字符串如 `"/host_node"`
- `parent`**NodeSlot**,填路径字符串如 `"/workstation/deck"`
- `class_name`**ClassSlot**,填类名如 `"container"`
### `auto-test_resource`
测试资源系统,返回当前资源树和设备列表
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/test_resource.json`](actions/test_resource.json)
- **可选参数**: `resource`, `resources`, `device`, `devices`
- **占位符字段**:
- `resource`**ResourceSlot**,单个物料节点 `{id, name, uuid}`
- `resources`**ResourceSlot**,物料节点数组 `[{id, name, uuid}, ...]`
- `device`**DeviceSlot**,设备路径字符串
- `devices`**DeviceSlot**,设备路径字符串
---
## 系统工具
### `test_latency`
测试设备通信延迟,返回 RTT、时间差、任务延迟等指标
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/test_latency.json`](actions/test_latency.json)
- **参数**: 无(零参数调用)
---
## 人工确认
### `manual_confirm`
创建人工确认节点,等待用户手动确认后继续
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/manual_confirm.json`](actions/manual_confirm.json)
- **核心参数**: `timeout_seconds`(超时时间,秒), `assignee_user_ids`(指派用户 ID 列表)
- **占位符字段**: `assignee_user_ids``unilabos_manual_confirm` 类型

View File

@@ -0,0 +1,93 @@
{
"type": "ResourceCreateFromOuterEasy",
"goal": {
"res_id": "res_id",
"class_name": "class_name",
"parent": "parent",
"device_id": "device_id",
"bind_locations": "bind_locations",
"liquid_input_slot": "liquid_input_slot[]",
"liquid_type": "liquid_type[]",
"liquid_volume": "liquid_volume[]",
"slot_on_deck": "slot_on_deck"
},
"schema": {
"type": "object",
"properties": {
"res_id": {
"type": "string"
},
"device_id": {
"type": "string"
},
"class_name": {
"type": "string"
},
"parent": {
"type": "string"
},
"bind_locations": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "bind_locations",
"additionalProperties": false
},
"liquid_input_slot": {
"type": "array",
"items": {
"type": "integer"
}
},
"liquid_type": {
"type": "array",
"items": {
"type": "string"
}
},
"liquid_volume": {
"type": "array",
"items": {
"type": "number"
}
},
"slot_on_deck": {
"type": "string"
}
},
"required": [],
"_unilabos_placeholder_info": {
"res_id": "unilabos_resources",
"device_id": "unilabos_devices",
"parent": "unilabos_nodes",
"class_name": "unilabos_class"
}
},
"goal_default": {},
"placeholder_keys": {
"res_id": "unilabos_resources",
"device_id": "unilabos_devices",
"parent": "unilabos_nodes",
"class_name": "unilabos_class"
}
}

View File

@@ -0,0 +1,32 @@
{
"type": "UniLabJsonCommand",
"goal": {
"timeout_seconds": "timeout_seconds",
"assignee_user_ids": "assignee_user_ids"
},
"schema": {
"type": "object",
"properties": {
"timeout_seconds": {
"type": "integer"
},
"assignee_user_ids": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"timeout_seconds",
"assignee_user_ids"
],
"_unilabos_placeholder_info": {
"assignee_user_ids": "unilabos_manual_confirm"
}
},
"goal_default": {},
"placeholder_keys": {
"assignee_user_ids": "unilabos_manual_confirm"
}
}

View File

@@ -0,0 +1,11 @@
{
"type": "UniLabJsonCommand",
"goal": {},
"schema": {
"type": "object",
"properties": {},
"required": []
},
"goal_default": {},
"placeholder_keys": {}
}

View File

@@ -0,0 +1,255 @@
{
"type": "UniLabJsonCommand",
"goal": {
"resource": "resource",
"resources": "resources",
"device": "device",
"devices": "devices"
},
"schema": {
"type": "object",
"properties": {
"resource": {
"type": "object",
"additionalProperties": false,
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"sample_id": {
"type": "string"
},
"children": {
"type": "array",
"items": {
"type": "string"
}
},
"parent": {
"type": "string"
},
"type": {
"type": "string"
},
"category": {
"type": "string"
},
"pose": {
"type": "object",
"properties": {
"position": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "position",
"additionalProperties": false
},
"orientation": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"w": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z",
"w"
],
"title": "orientation",
"additionalProperties": false
}
},
"required": [
"position",
"orientation"
],
"title": "pose",
"additionalProperties": false
},
"config": {
"type": "string"
},
"data": {
"type": "string"
}
},
"title": "resource"
},
"resources": {
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"sample_id": {
"type": "string"
},
"children": {
"type": "array",
"items": {
"type": "string"
}
},
"parent": {
"type": "string"
},
"type": {
"type": "string"
},
"category": {
"type": "string"
},
"pose": {
"type": "object",
"properties": {
"position": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "position",
"additionalProperties": false
},
"orientation": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"w": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z",
"w"
],
"title": "orientation",
"additionalProperties": false
}
},
"required": [
"position",
"orientation"
],
"title": "pose",
"additionalProperties": false
},
"config": {
"type": "string"
},
"data": {
"type": "string"
}
},
"title": "resources"
},
"type": "array"
},
"device": {
"type": "string",
"description": "device reference"
},
"devices": {
"type": "string",
"description": "device reference"
}
},
"required": [],
"_unilabos_placeholder_info": {
"resource": "unilabos_resources",
"resources": "unilabos_resources",
"device": "unilabos_devices",
"devices": "unilabos_devices"
}
},
"goal_default": {},
"placeholder_keys": {
"resource": "unilabos_resources",
"resources": "unilabos_resources",
"device": "unilabos_devices",
"devices": "unilabos_devices"
}
}

View File

@@ -1,11 +1,13 @@
---
name: submit-agent-result
description: Submit historical experiment results (agent_result) to Uni-Lab notebook — read data files, assemble JSON payload, PUT to cloud API. Use when the user wants to submit experiment results, upload agent results, report experiment data, or mentions agent_result/实验结果/历史记录/notebook结果.
description: Submit historical experiment results (agent_result) to Uni-Lab cloud platform (leap-lab) notebook — read data files, assemble JSON payload, PUT to cloud API. Use when the user wants to submit experiment results, upload agent results, report experiment data, or mentions agent_result/实验结果/历史记录/notebook结果.
---
# 提交历史实验记录指南
# Uni-Lab 提交历史实验记录指南
通过云端 API 向已创建的 notebook 提交实验结果数据agent_result。支持从 JSON / CSV 文件读取数据,整合后提交。
通过 Uni-Lab 云端 API 向已创建的 notebook 提交实验结果数据agent_result。支持从 JSON / CSV 文件读取数据,整合后提交。
> **重要**:本指南中的 `Authorization: Lab <token>` 是 **Uni-Lab 平台专用的认证方式**`Lab` 是 Uni-Lab 的 auth scheme 关键字,**不是** HTTP Basic 认证。请勿将其替换为 `Basic`。
## 前置条件(缺一不可)
@@ -18,23 +20,26 @@ description: Submit historical experiment results (agent_result) to Uni-Lab note
生成 AUTH token
```bash
# ⚠️ 注意scheme 是 "Lab"Uni-Lab 专用),不是 "Basic"
python -c "import base64,sys; print(base64.b64encode(f'{sys.argv[1]}:{sys.argv[2]}'.encode()).decode())" <ak> <sk>
```
输出即为 token 值,拼接为 `Authorization: Lab <token>`
输出即为 token 值,拼接为 `Authorization: Lab <token>``Lab` 是 Uni-Lab 平台 auth scheme不可替换为 `Basic`
### 2. --addr → BASE URL
| `--addr` 值 | BASE |
|-------------|------|
| `test` | `https://uni-lab.test.bohrium.com` |
| `uat` | `https://uni-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://uni-lab.bohrium.com` |
| `--addr` | BASE |
| ------------ | ----------------------------------- |
| `test` | `https://leap-lab.test.bohrium.com` |
| `uat` | `https://leap-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://leap-lab.bohrium.com` |
确认后设置:
```bash
BASE="<根据 addr 确定的 URL>"
# ⚠️ Auth scheme 必须是 "Lab"Uni-Lab 专用),不是 "Basic"
AUTH="Authorization: Lab <上面命令输出的 token>"
```
@@ -45,6 +50,7 @@ AUTH="Authorization: Lab <上面命令输出的 token>"
notebook_uuid 来自之前通过「批量提交实验」创建的实验批次,即 `POST /api/v1/lab/notebook` 返回的 `data.uuid`
如果用户不记得,可提示:
- 查看之前的对话记录中创建 notebook 时返回的 UUID
- 或通过平台页面查找对应的 notebook
@@ -54,11 +60,11 @@ notebook_uuid 来自之前通过「批量提交实验」创建的实验批次,
用户需要提供实验结果数据,支持以下方式:
| 方式 | 说明 |
|------|------|
| JSON 文件 | 直接作为 `agent_result` 的内容合并 |
| CSV 文件 | 转为 `{"文件名": [行数据...]}` 格式 |
| 手动指定 | 用户直接告知 key-value 数据,由 agent 构建 JSON |
| 方式 | 说明 |
| --------- | ----------------------------------------------- |
| JSON 文件 | 直接作为 `agent_result` 的内容合并 |
| CSV 文件 | 转为 `{"文件名": [行数据...]}` 格式 |
| 手动指定 | 用户直接告知 key-value 数据,由 agent 构建 JSON |
**四项全部就绪后才可开始。**
@@ -90,7 +96,7 @@ curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"
返回:
```json
{"code": 0, "data": {"uuid": "xxx", "name": "实验室名称"}}
{ "code": 0, "data": { "uuid": "xxx", "name": "实验室名称" } }
```
记住 `data.uuid``lab_uuid`
@@ -121,42 +127,45 @@ curl -s -X PUT "$BASE/api/v1/lab/notebook/agent-result" \
#### 必要字段
| 字段 | 类型 | 说明 |
|------|------|------|
| 字段 | 类型 | 说明 |
| --------------- | ------------- | ------------------------------------------- |
| `notebook_uuid` | string (UUID) | 目标 notebook 的 UUID从批量提交实验时获取 |
| `agent_result` | object | 实验结果数据,任意 JSON 对象 |
| `agent_result` | object | 实验结果数据,任意 JSON 对象 |
#### agent_result 内容格式
`agent_result` 接受**任意 JSON 对象**,常见格式:
**简单键值对**
```json
{
"avg_rtt_ms": 12.5,
"status": "success",
"test_count": 5
"avg_rtt_ms": 12.5,
"status": "success",
"test_count": 5
}
```
**包含嵌套结构**
```json
{
"summary": {"total": 100, "passed": 98, "failed": 2},
"measurements": [
{"sample_id": "S001", "value": 3.14, "unit": "mg/mL"},
{"sample_id": "S002", "value": 2.71, "unit": "mg/mL"}
]
"summary": { "total": 100, "passed": 98, "failed": 2 },
"measurements": [
{ "sample_id": "S001", "value": 3.14, "unit": "mg/mL" },
{ "sample_id": "S002", "value": 2.71, "unit": "mg/mL" }
]
}
```
**从 CSV 文件导入**(脚本自动转换):
```json
{
"experiment_data": [
{"温度": 25, "压力": 101.3, "产率": 0.85},
{"温度": 30, "压力": 101.3, "产率": 0.91}
]
"experiment_data": [
{ "温度": 25, "压力": 101.3, "产率": 0.85 },
{ "温度": 30, "压力": 101.3, "产率": 0.91 }
]
}
```
@@ -178,22 +187,22 @@ python scripts/prepare_agent_result.py \
[--output <output.json>]
```
| 参数 | 必选 | 说明 |
|------|------|------|
| `--notebook-uuid` | 是 | 目标 notebook UUID |
| `--files` | 是 | 输入文件路径支持多个JSON / CSV |
| `--auth` | 提交时必选 | Lab tokenbase64(ak:sk) |
| `--base` | 提交时必选 | API base URL |
| `--submit` | 否 | 加上此标志则直接提交到云端 |
| `--output` | 否 | 输出 JSON 路径(默认 `agent_result_body.json` |
| 参数 | 必选 | 说明 |
| ----------------- | ---------- | ----------------------------------------------- |
| `--notebook-uuid` | 是 | 目标 notebook UUID |
| `--files` | 是 | 输入文件路径支持多个JSON / CSV |
| `--auth` | 提交时必选 | Lab tokenbase64(ak:sk) |
| `--base` | 提交时必选 | API base URL |
| `--submit` | 否 | 加上此标志则直接提交到云端 |
| `--output` | 否 | 输出 JSON 路径(默认 `agent_result_body.json` |
### 文件合并规则
| 文件类型 | 合并方式 |
|----------|----------|
| `.json`dict | 字段直接合并到 `agent_result` 顶层 |
| `.json`list/other | 以文件名为 key 放入 `agent_result` |
| `.csv` | 以文件名(不含扩展名)为 key值为行对象数组 |
| 文件类型 | 合并方式 |
| --------------------- | -------------------------------------------- |
| `.json`dict | 字段直接合并到 `agent_result` 顶层 |
| `.json`list/other | 以文件名为 key 放入 `agent_result` |
| `.csv` | 以文件名(不含扩展名)为 key值为行对象数组 |
多个文件的字段会合并。JSON dict 中的重复 key 后者覆盖前者。
@@ -210,7 +219,7 @@ python scripts/prepare_agent_result.py \
--notebook-uuid 73c67dca-c8cc-4936-85a0-329106aa7cca \
--files results.json \
--auth YTFmZDlkNGUt... \
--base https://uni-lab.test.bohrium.com \
--base https://leap-lab.test.bohrium.com \
--submit
```
@@ -272,4 +281,4 @@ Task Progress:
### Q: 认证方式是 Lab 还是 Api
本指南统一使用 `Authorization: Lab <base64(ak:sk)>` 方式。如果用户有独立的 API Key也可用 `Authorization: Api <key>` 替代。
本指南统一使用 `Authorization: Lab <base64(ak:sk)>` 方式`Lab` 是 Uni-Lab 平台的 auth scheme**绝不能用 `Basic` 替代**。如果用户有独立的 API Key也可用 `Authorization: Api <key>` 替代。

View File

@@ -0,0 +1,272 @@
---
name: virtual-workbench
description: Operate Virtual Workbench via REST API — prepare materials, move to heating stations, start heating, move to output, transfer resources. Use when the user mentions virtual workbench, virtual_workbench, 虚拟工作台, heating stations, material processing, or workbench operations.
---
# Virtual Workbench API Skill
## 设备信息
- **device_id**: `virtual_workbench`
- **Python 源码**: `unilabos/devices/virtual/workbench.py`
- **设备类**: `VirtualWorkbench`
- **动作数**: 6`auto-prepare_materials`, `auto-move_to_heating_station`, `auto-start_heating`, `auto-move_to_output`, `transfer`, `manual_confirm`
- **设备描述**: 模拟工作台,包含 1 个机械臂(每次操作 2s独占锁和 3 个加热台(每次加热 60s可并行
### 典型工作流程
1. `prepare_materials` — 生成 A1-A5 物料5 个 output handle
2. `move_to_heating_station` — 物料并发竞争机械臂,移动到空闲加热台
3. `start_heating` — 启动加热3 个加热台可并行)
4. `move_to_output` — 加热完成后移到输出位置 Cn
## 前置条件(缺一不可)
使用本 skill 前,**必须**先确认以下信息。如果缺少任何一项,**立即向用户询问并终止**,等补齐后再继续。
### 1. ak / sk → AUTH
从启动参数 `--ak` `--sk` 或 config.py 中获取,生成 token`base64(ak:sk)``Authorization: Lab <token>`
### 2. --addr → BASE URL
| `--addr` 值 | BASE |
| ------------ | ----------------------------------- |
| `test` | `https://leap-lab.test.bohrium.com` |
| `uat` | `https://leap-lab.uat.bohrium.com` |
| `local` | `http://127.0.0.1:48197` |
| 不传(默认) | `https://leap-lab.bohrium.com` |
确认后设置:
```bash
BASE="<根据 addr 确定的 URL>"
AUTH="Authorization: Lab <token>"
```
**两项全部就绪后才可发起 API 请求。**
## Session State
- `lab_uuid` — 实验室 UUID首次通过 API #1 自动获取,**不需要问用户**
- `device_name``virtual_workbench`
## 请求约定
所有请求使用 `curl -s`POST/PATCH/DELETE 需加 `Content-Type: application/json`
> **Windows 平台**必须使用 `curl.exe`(而非 PowerShell 的 `curl` 别名)。
---
## API Endpoints
### 1. 获取实验室信息(自动获取 lab_uuid
```bash
curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"
```
返回 `data.uuid``lab_uuid``data.name``lab_name`
### 2. 创建工作流
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/owner" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"name":"<名称>","lab_uuid":"<lab_uuid>","description":"<描述>"}'
```
返回 `data.uuid``workflow_uuid`。创建成功后告知用户链接:`$BASE/laboratory/$lab_uuid/workflow/$workflow_uuid`
### 3. 创建节点
```bash
curl -s -X POST "$BASE/api/v1/edge/workflow/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"workflow_uuid":"<workflow_uuid>","resource_template_name":"virtual_workbench","node_template_name":"<action_name>"}'
```
- `resource_template_name` 固定为 `virtual_workbench`
- `node_template_name` — action 名称(如 `auto-prepare_materials`, `auto-move_to_heating_station`
### 4. 删除节点
```bash
curl -s -X DELETE "$BASE/api/v1/lab/workflow/nodes" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"node_uuids":["<uuid1>"],"workflow_uuid":"<workflow_uuid>"}'
```
### 5. 更新节点参数
```bash
curl -s -X PATCH "$BASE/api/v1/lab/workflow/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"workflow_uuid":"<wf_uuid>","uuid":"<node_uuid>","param":{...}}'
```
参考 [action-index.md](action-index.md) 确定哪些字段是 Slot。
### 6. 查询节点 handles
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/node-handles" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"node_uuids":["<node_uuid_1>","<node_uuid_2>"]}'
```
### 7. 批量创建边
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/edges" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"edges":[{"source_node_uuid":"<uuid>","target_node_uuid":"<uuid>","source_handle_uuid":"<uuid>","target_handle_uuid":"<uuid>"}]}'
```
### 8. 启动工作流
```bash
curl -s -X POST "$BASE/api/v1/lab/workflow/<workflow_uuid>/run" -H "$AUTH"
```
### 9. 运行设备单动作
```bash
curl -s -X POST "$BASE/api/v1/lab/mcp/run/action" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"lab_uuid":"<lab_uuid>","device_id":"virtual_workbench","action":"<action_name>","action_type":"<type>","param":{...}}'
```
`param` 直接放 goal 里的属性,**不要**再包一层 `{"goal": {...}}`
> **WARNING: `action_type` 必须正确,传错会导致任务永远卡住无法完成。** 从下表或 `actions/<name>.json` 的 `type` 字段获取。
#### action_type 速查表
| action | action_type |
|--------|-------------|
| `auto-prepare_materials` | `UniLabJsonCommand` |
| `auto-move_to_heating_station` | `UniLabJsonCommand` |
| `auto-start_heating` | `UniLabJsonCommand` |
| `auto-move_to_output` | `UniLabJsonCommand` |
| `transfer` | `UniLabJsonCommandAsync` |
| `manual_confirm` | `UniLabJsonCommand` |
### 10. 查询任务状态
```bash
curl -s -X GET "$BASE/api/v1/lab/mcp/task/<task_uuid>" -H "$AUTH"
```
### 11. 运行工作流单节点
```bash
curl -s -X POST "$BASE/api/v1/lab/mcp/run/workflow/action" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"node_uuid":"<node_uuid>"}'
```
### 12. 获取资源树(物料信息)
```bash
curl -s -X GET "$BASE/api/v1/lab/material/download/$lab_uuid" -H "$AUTH"
```
注意 `lab_uuid` 在路径中。返回 `data.nodes[]` 含所有节点(设备 + 物料),每个节点含 `name``uuid``type``parent`
### 13. 获取工作流模板详情
```bash
curl -s -X GET "$BASE/api/v1/lab/workflow/template/detail/$workflow_uuid" -H "$AUTH"
```
> 必须使用 `/lab/workflow/template/detail/{uuid}`,其他路径会返回 404。
### 14. 按名称查询物料模板
```bash
curl -s -X GET "$BASE/api/v1/lab/material/template/by-name?lab_uuid=$lab_uuid&name=<template_name>" -H "$AUTH"
```
返回 `data.uuid``res_template_uuid`,用于 API #15
### 15. 创建物料节点
```bash
curl -s -X POST "$BASE/api/v1/edge/material/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"res_template_uuid":"<uuid>","name":"<名称>","display_name":"<显示名>","parent_uuid":"<父节点uuid>","data":{...}}'
```
### 16. 更新物料节点
```bash
curl -s -X PUT "$BASE/api/v1/edge/material/node" \
-H "$AUTH" -H "Content-Type: application/json" \
-d '{"uuid":"<节点uuid>","display_name":"<新名称>","data":{...}}'
```
---
## Placeholder Slot 填写规则
| `placeholder_keys` 值 | Slot 类型 | 填写格式 | 选取范围 |
| --------------------- | ------------ | ----------------------------------------------------- | ---------------------- |
| `unilabos_resources` | ResourceSlot | `{"id": "/path/name", "name": "name", "uuid": "xxx"}` | 仅物料节点(非设备) |
| `unilabos_devices` | DeviceSlot | `"/parent/device_name"` | 仅设备节点type=device |
| `unilabos_nodes` | NodeSlot | `"/parent/node_name"` | 所有节点(设备 + 物料) |
| `unilabos_class` | ClassSlot | `"class_name"` | 注册表中已注册的资源类 |
### virtual_workbench 设备的 Slot 字段表
| Action | 字段 | Slot 类型 | 说明 |
| ----------------- | ---------------- | ------------ | -------------------- |
| `transfer` | `resource` | ResourceSlot | 待转移物料数组 |
| `transfer` | `target_device` | DeviceSlot | 目标设备路径 |
| `transfer` | `mount_resource` | ResourceSlot | 目标孔位数组 |
| `manual_confirm` | `resource` | ResourceSlot | 确认用物料数组 |
| `manual_confirm` | `target_device` | DeviceSlot | 确认用目标设备 |
| `manual_confirm` | `mount_resource` | ResourceSlot | 确认用目标孔位数组 |
> `prepare_materials`、`move_to_heating_station`、`start_heating`、`move_to_output` 这 4 个动作**无 Slot 字段**,参数为纯数值/整数。
---
## 渐进加载策略
1. **SKILL.md**(本文件)— API 端点 + session state 管理 + 设备工作流概览
2. **[action-index.md](action-index.md)** — 按分类浏览 6 个动作的描述和核心参数
3. **[actions/\<name\>.json](actions/)** — 仅在需要构建具体请求时,加载对应 action 的完整 JSON Schema
---
## 完整工作流 Checklist
```
Task Progress:
- [ ] Step 1: GET /edge/lab/info 获取 lab_uuid
- [ ] Step 2: 获取资源树 (GET #12) → 记住可用物料
- [ ] Step 3: 读 action-index.md 确定要用的 action 名
- [ ] Step 4: 创建工作流 (POST #2) → 记住 workflow_uuid告知用户链接
- [ ] Step 5: 创建节点 (POST #3, resource_template_name=virtual_workbench) → 记住 node_uuid + data.param
- [ ] Step 6: 根据 _unilabos_placeholder_info 和资源树,填写 data.param 中的 Slot 字段
- [ ] Step 7: 更新节点参数 (PATCH #5)
- [ ] Step 8: 查询节点 handles (POST #6) → 获取各节点的 handle_uuid
- [ ] Step 9: 批量创建边 (POST #7) → 用 handle_uuid 连接节点
- [ ] Step 10: 启动工作流 (POST #8) 或运行单节点 (POST #11)
- [ ] Step 11: 查询任务状态 (GET #10) 确认完成
```
### 典型 5 物料并发加热工作流示例
```
prepare_materials (count=5)
├─ channel_1 → move_to_heating_station (material_number=1) → start_heating → move_to_output
├─ channel_2 → move_to_heating_station (material_number=2) → start_heating → move_to_output
├─ channel_3 → move_to_heating_station (material_number=3) → start_heating → move_to_output
├─ channel_4 → move_to_heating_station (material_number=4) → start_heating → move_to_output
└─ channel_5 → move_to_heating_station (material_number=5) → start_heating → move_to_output
```
创建节点时,`prepare_materials` 的 5 个 output handle`channel_1` ~ `channel_5`)分别连接到 5 个 `move_to_heating_station` 节点的 `material_input` handle。每个 `move_to_heating_station``heating_station_output``material_number_output` 连接到对应 `start_heating``station_id_input``material_number_input`

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# Action Index — virtual_workbench
6 个动作,按功能分类。每个动作的完整 JSON Schema 在 `actions/<name>.json`
---
## 物料准备
### `auto-prepare_materials`
批量准备物料(虚拟起始节点),生成 A1-A5 物料编号,输出 5 个 handle 供后续节点使用
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/prepare_materials.json`](actions/prepare_materials.json)
- **可选参数**: `count`(物料数量,默认 5
---
## 机械臂 & 加热台操作
### `auto-move_to_heating_station`
将物料从 An 位置移动到空闲加热台(竞争机械臂,自动查找空闲加热台)
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/move_to_heating_station.json`](actions/move_to_heating_station.json)
- **核心参数**: `material_number`物料编号integer
### `auto-start_heating`
启动指定加热台的加热程序可并行3 个加热台同时工作)
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/start_heating.json`](actions/start_heating.json)
- **核心参数**: `station_id`(加热台 ID`material_number`(物料编号)
### `auto-move_to_output`
将加热完成的物料从加热台移动到输出位置 Cn
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/move_to_output.json`](actions/move_to_output.json)
- **核心参数**: `station_id`(加热台 ID`material_number`(物料编号)
---
## 物料转移
### `transfer`
异步转移物料到目标设备(通过 ROS 资源转移)
- **action_type**: `UniLabJsonCommandAsync`
- **Schema**: [`actions/transfer.json`](actions/transfer.json)
- **核心参数**: `resource`, `target_device`, `mount_resource`
- **占位符字段**:
- `resource`**ResourceSlot**,待转移的物料数组 `[{id, name, uuid}, ...]`
- `target_device`**DeviceSlot**,目标设备路径字符串
- `mount_resource`**ResourceSlot**,目标孔位数组 `[{id, name, uuid}, ...]`
---
## 人工确认
### `manual_confirm`
创建人工确认节点,等待用户手动确认后继续(含物料转移上下文)
- **action_type**: `UniLabJsonCommand`
- **Schema**: [`actions/manual_confirm.json`](actions/manual_confirm.json)
- **核心参数**: `resource`, `target_device`, `mount_resource`, `timeout_seconds`, `assignee_user_ids`
- **占位符字段**:
- `resource`**ResourceSlot**,物料数组
- `target_device`**DeviceSlot**,目标设备路径
- `mount_resource`**ResourceSlot**,目标孔位数组
- `assignee_user_ids``unilabos_manual_confirm` 类型

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{
"type": "UniLabJsonCommand",
"goal": {
"resource": "resource",
"target_device": "target_device",
"mount_resource": "mount_resource",
"timeout_seconds": "timeout_seconds",
"assignee_user_ids": "assignee_user_ids"
},
"schema": {
"type": "object",
"properties": {
"resource": {
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"sample_id": {
"type": "string"
},
"children": {
"type": "array",
"items": {
"type": "string"
}
},
"parent": {
"type": "string"
},
"type": {
"type": "string"
},
"category": {
"type": "string"
},
"pose": {
"type": "object",
"properties": {
"position": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "position",
"additionalProperties": false
},
"orientation": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"w": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z",
"w"
],
"title": "orientation",
"additionalProperties": false
}
},
"required": [
"position",
"orientation"
],
"title": "pose",
"additionalProperties": false
},
"config": {
"type": "string"
},
"data": {
"type": "string"
}
},
"title": "resource"
},
"type": "array"
},
"target_device": {
"type": "string",
"description": "device reference"
},
"mount_resource": {
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"sample_id": {
"type": "string"
},
"children": {
"type": "array",
"items": {
"type": "string"
}
},
"parent": {
"type": "string"
},
"type": {
"type": "string"
},
"category": {
"type": "string"
},
"pose": {
"type": "object",
"properties": {
"position": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "position",
"additionalProperties": false
},
"orientation": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"w": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z",
"w"
],
"title": "orientation",
"additionalProperties": false
}
},
"required": [
"position",
"orientation"
],
"title": "pose",
"additionalProperties": false
},
"config": {
"type": "string"
},
"data": {
"type": "string"
}
},
"title": "mount_resource"
},
"type": "array"
},
"timeout_seconds": {
"type": "integer"
},
"assignee_user_ids": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"resource",
"target_device",
"mount_resource",
"timeout_seconds",
"assignee_user_ids"
],
"_unilabos_placeholder_info": {
"resource": "unilabos_resources",
"target_device": "unilabos_devices",
"mount_resource": "unilabos_resources",
"assignee_user_ids": "unilabos_manual_confirm"
}
},
"goal_default": {},
"placeholder_keys": {
"resource": "unilabos_resources",
"target_device": "unilabos_devices",
"mount_resource": "unilabos_resources",
"assignee_user_ids": "unilabos_manual_confirm"
}
}

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{
"type": "UniLabJsonCommand",
"goal": {
"material_number": "material_number"
},
"schema": {
"type": "object",
"properties": {
"material_number": {
"type": "integer"
}
},
"required": [
"material_number"
]
},
"goal_default": {},
"placeholder_keys": {}
}

View File

@@ -0,0 +1,24 @@
{
"type": "UniLabJsonCommand",
"goal": {
"station_id": "station_id",
"material_number": "material_number"
},
"schema": {
"type": "object",
"properties": {
"station_id": {
"type": "integer"
},
"material_number": {
"type": "integer"
}
},
"required": [
"station_id",
"material_number"
]
},
"goal_default": {},
"placeholder_keys": {}
}

View File

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{
"type": "UniLabJsonCommand",
"goal": {
"count": "count"
},
"schema": {
"type": "object",
"properties": {
"count": {
"type": "integer",
"default": 5
}
},
"required": []
},
"goal_default": {
"count": 5
},
"placeholder_keys": {}
}

View File

@@ -0,0 +1,24 @@
{
"type": "UniLabJsonCommand",
"goal": {
"station_id": "station_id",
"material_number": "material_number"
},
"schema": {
"type": "object",
"properties": {
"station_id": {
"type": "integer"
},
"material_number": {
"type": "integer"
}
},
"required": [
"station_id",
"material_number"
]
},
"goal_default": {},
"placeholder_keys": {}
}

View File

@@ -0,0 +1,255 @@
{
"type": "UniLabJsonCommandAsync",
"goal": {
"resource": "resource",
"target_device": "target_device",
"mount_resource": "mount_resource"
},
"schema": {
"type": "object",
"properties": {
"resource": {
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"sample_id": {
"type": "string"
},
"children": {
"type": "array",
"items": {
"type": "string"
}
},
"parent": {
"type": "string"
},
"type": {
"type": "string"
},
"category": {
"type": "string"
},
"pose": {
"type": "object",
"properties": {
"position": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "position",
"additionalProperties": false
},
"orientation": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"w": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z",
"w"
],
"title": "orientation",
"additionalProperties": false
}
},
"required": [
"position",
"orientation"
],
"title": "pose",
"additionalProperties": false
},
"config": {
"type": "string"
},
"data": {
"type": "string"
}
},
"title": "resource"
},
"type": "array"
},
"target_device": {
"type": "string",
"description": "device reference"
},
"mount_resource": {
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"sample_id": {
"type": "string"
},
"children": {
"type": "array",
"items": {
"type": "string"
}
},
"parent": {
"type": "string"
},
"type": {
"type": "string"
},
"category": {
"type": "string"
},
"pose": {
"type": "object",
"properties": {
"position": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z"
],
"title": "position",
"additionalProperties": false
},
"orientation": {
"type": "object",
"properties": {
"x": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"y": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"z": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
},
"w": {
"type": "number",
"minimum": -1.7976931348623157e+308,
"maximum": 1.7976931348623157e+308
}
},
"required": [
"x",
"y",
"z",
"w"
],
"title": "orientation",
"additionalProperties": false
}
},
"required": [
"position",
"orientation"
],
"title": "pose",
"additionalProperties": false
},
"config": {
"type": "string"
},
"data": {
"type": "string"
}
},
"title": "mount_resource"
},
"type": "array"
}
},
"required": [
"resource",
"target_device",
"mount_resource"
],
"_unilabos_placeholder_info": {
"resource": "unilabos_resources",
"target_device": "unilabos_devices",
"mount_resource": "unilabos_resources"
}
},
"goal_default": {},
"placeholder_keys": {
"resource": "unilabos_resources",
"target_device": "unilabos_devices",
"mount_resource": "unilabos_resources"
}
}

View File

@@ -12,7 +12,7 @@ Uni-Lab 使用 Python 格式的配置文件(`.py`),默认为 `unilabos_dat
**获取方式:**
进入 [Uni-Lab 实验室](https://uni-lab.bohrium.com),点击左下角的头像,在实验室详情中获取所在实验室的 ak 和 sk
进入 [Uni-Lab 实验室](https://leap-lab.bohrium.com),点击左下角的头像,在实验室详情中获取所在实验室的 ak 和 sk
![copy_aksk.gif](image/copy_aksk.gif)
@@ -69,7 +69,7 @@ class WSConfig:
# HTTP配置
class HTTPConfig:
remote_addr = "https://uni-lab.bohrium.com/api/v1" # 远程服务器地址
remote_addr = "https://leap-lab.bohrium.com/api/v1" # 远程服务器地址
# ROS配置
class ROSConfig:
@@ -209,8 +209,8 @@ unilab --ak "key" --sk "secret" --addr "test" --upload_registry --2d_vis -g grap
`--addr` 参数支持以下预设值,会自动转换为对应的完整 URL
- `test``https://uni-lab.test.bohrium.com/api/v1`
- `uat``https://uni-lab.uat.bohrium.com/api/v1`
- `test``https://leap-lab.test.bohrium.com/api/v1`
- `uat``https://leap-lab.uat.bohrium.com/api/v1`
- `local``http://127.0.0.1:48197/api/v1`
- 其他值 → 直接使用作为完整 URL
@@ -248,7 +248,7 @@ unilab --ak "key" --sk "secret" --addr "test" --upload_registry --2d_vis -g grap
`ak``sk` 是必需的认证参数:
1. **获取方式**:在 [Uni-Lab 官网](https://uni-lab.bohrium.com) 注册实验室后获得
1. **获取方式**:在 [Uni-Lab 官网](https://leap-lab.bohrium.com) 注册实验室后获得
2. **配置方式**
- **命令行参数**`--ak "your_key" --sk "your_secret"`(最高优先级,推荐)
- **环境变量**`UNILABOS_BASICCONFIG_AK``UNILABOS_BASICCONFIG_SK`
@@ -275,15 +275,15 @@ WebSocket 是 Uni-Lab 的主要通信方式:
HTTP 客户端配置用于与云端服务通信:
| 参数 | 类型 | 默认值 | 说明 |
| ------------- | ---- | -------------------------------------- | ------------ |
| `remote_addr` | str | `"https://uni-lab.bohrium.com/api/v1"` | 远程服务地址 |
| 参数 | 类型 | 默认值 | 说明 |
| ------------- | ---- | --------------------------------------- | ------------ |
| `remote_addr` | str | `"https://leap-lab.bohrium.com/api/v1"` | 远程服务地址 |
**预设环境地址**
- 生产环境:`https://uni-lab.bohrium.com/api/v1`(默认)
- 测试环境:`https://uni-lab.test.bohrium.com/api/v1`
- UAT 环境:`https://uni-lab.uat.bohrium.com/api/v1`
- 生产环境:`https://leap-lab.bohrium.com/api/v1`(默认)
- 测试环境:`https://leap-lab.test.bohrium.com/api/v1`
- UAT 环境:`https://leap-lab.uat.bohrium.com/api/v1`
- 本地环境:`http://127.0.0.1:48197/api/v1`
### 4. ROSConfig - ROS 配置
@@ -401,7 +401,7 @@ export UNILABOS_WSCONFIG_RECONNECT_INTERVAL="10"
export UNILABOS_WSCONFIG_MAX_RECONNECT_ATTEMPTS="500"
# 设置HTTP配置
export UNILABOS_HTTPCONFIG_REMOTE_ADDR="https://uni-lab.test.bohrium.com/api/v1"
export UNILABOS_HTTPCONFIG_REMOTE_ADDR="https://leap-lab.test.bohrium.com/api/v1"
```
## 配置文件使用方法
@@ -484,13 +484,13 @@ export UNILABOS_WSCONFIG_MAX_RECONNECT_ATTEMPTS=100
```python
class HTTPConfig:
remote_addr = "https://uni-lab.test.bohrium.com/api/v1"
remote_addr = "https://leap-lab.test.bohrium.com/api/v1"
```
**环境变量方式:**
```bash
export UNILABOS_HTTPCONFIG_REMOTE_ADDR=https://uni-lab.test.bohrium.com/api/v1
export UNILABOS_HTTPCONFIG_REMOTE_ADDR=https://leap-lab.test.bohrium.com/api/v1
```
**命令行方式(推荐):**

View File

@@ -23,7 +23,7 @@ Uni-Lab-OS 支持多种部署模式:
```
┌──────────────────────────────────────────────┐
│ Cloud Platform/Self-hosted Platform │
uni-lab.bohrium.com │
leap-lab.bohrium.com │
│ (Resource Management, Task Scheduling, │
│ Monitoring) │
└────────────────────┬─────────────────────────┘
@@ -444,7 +444,7 @@ ros2 daemon stop && ros2 daemon start
```bash
# 测试云端连接
curl https://uni-lab.bohrium.com/api/v1/health
curl https://leap-lab.bohrium.com/api/v1/health
# 测试WebSocket
# 启动Uni-Lab后查看日志

View File

@@ -33,11 +33,11 @@
**选择合适的安装包:**
| 安装包 | 适用场景 | 包含组件 |
|--------|----------|----------|
| `unilabos` | **推荐大多数用户**,生产部署 | 完整安装包,开箱即用 |
| `unilabos-env` | 开发者(可编辑安装) | 仅环境依赖,通过 pip 安装 unilabos |
| `unilabos-full` | 仿真/可视化 | unilabos + 完整 ROS2 桌面版 + Gazebo + MoveIt |
| 安装包 | 适用场景 | 包含组件 |
| --------------- | ---------------------------- | --------------------------------------------- |
| `unilabos` | **推荐大多数用户**,生产部署 | 完整安装包,开箱即用 |
| `unilabos-env` | 开发者(可编辑安装) | 仅环境依赖,通过 pip 安装 unilabos |
| `unilabos-full` | 仿真/可视化 | unilabos + 完整 ROS2 桌面版 + Gazebo + MoveIt |
**关键步骤:**
@@ -66,6 +66,7 @@ mamba install uni-lab::unilabos-full -c robostack-staging -c conda-forge
```
**选择建议:**
- **日常使用/生产部署**:使用 `unilabos`(推荐),完整功能,开箱即用
- **开发者**:使用 `unilabos-env` + `pip install -e .` + `uv pip install -r unilabos/utils/requirements.txt`,代码修改立即生效
- **仿真/可视化**:使用 `unilabos-full`,含 Gazebo、rviz2、MoveIt
@@ -88,7 +89,7 @@ python -c "from unilabos_msgs.msg import Resource; print('ROS msgs OK')"
#### 2.1 注册实验室账号
1. 访问 [https://uni-lab.bohrium.com](https://uni-lab.bohrium.com)
1. 访问 [https://leap-lab.bohrium.com](https://leap-lab.bohrium.com)
2. 注册账号并登录
3. 创建新实验室
@@ -297,7 +298,7 @@ unilab --ak your_ak --sk your_sk -g test/experiments/mock_devices/mock_all.json
#### 5.2 访问 Web 界面
启动系统后,访问[https://uni-lab.bohrium.com](https://uni-lab.bohrium.com)
启动系统后,访问[https://leap-lab.bohrium.com](https://leap-lab.bohrium.com)
#### 5.3 添加设备和物料
@@ -306,12 +307,10 @@ unilab --ak your_ak --sk your_sk -g test/experiments/mock_devices/mock_all.json
**示例场景:** 创建一个简单的液体转移实验
1. **添加工作站(必需):**
- 在"仪器设备"中找到 `work_station`
- 添加 `workstation` x1
2. **添加虚拟转移泵:**
- 在"仪器设备"中找到 `virtual_device`
- 添加 `virtual_transfer_pump` x1
@@ -818,6 +817,7 @@ uv pip install -r unilabos/utils/requirements.txt
```
**为什么使用这种方式?**
- `unilabos-env` 提供 ROS2 核心组件和 uv通过 conda 安装,避免编译)
- `unilabos/utils/requirements.txt` 包含所有运行时需要的 pip 依赖
- `dev_install.py` 自动检测中文环境,中文系统自动使用清华镜像
@@ -1796,32 +1796,27 @@ unilab --ak your_ak --sk your_sk -g graph.json \
**详细步骤:**
1. **需求分析**
- 明确实验流程
- 列出所需设备和物料
- 设计工作流程图
2. **环境搭建**
- 安装 Uni-Lab-OS
- 创建实验室账号
- 准备开发工具IDE、Git
3. **原型验证**
- 使用虚拟设备测试流程
- 验证工作流逻辑
- 调整参数
4. **迭代开发**
- 实现自定义设备驱动(同时撰写单点函数测试)
- 编写注册表
- 单元测试
- 集成测试
5. **测试部署**
- 连接真实硬件
- 空跑测试
- 小规模试验
@@ -1871,7 +1866,7 @@ unilab --ak your_ak --sk your_sk -g graph.json \
#### 14.5 社区支持
- **GitHub Issues**[https://github.com/deepmodeling/Uni-Lab-OS/issues](https://github.com/deepmodeling/Uni-Lab-OS/issues)
- **官方网站**[https://uni-lab.bohrium.com](https://uni-lab.bohrium.com)
- **官方网站**[https://leap-lab.bohrium.com](https://leap-lab.bohrium.com)
---

View File

@@ -626,7 +626,7 @@ unilab
**云端图文件管理**:
1. 登录 https://uni-lab.bohrium.com
1. 登录 https://leap-lab.bohrium.com
2. 进入"设备配置"
3. 创建或编辑配置
4. 保存到云端

View File

@@ -54,7 +54,6 @@ Uni-Lab 的启动过程分为以下几个阶段:
您可以直接跟随 unilabos 的提示进行,无需查阅本节
- **工作目录设置**
- 如果当前目录以 `unilabos_data` 结尾,则使用当前目录
- 否则使用 `当前目录/unilabos_data` 作为工作目录
- 可通过 `--working_dir` 指定自定义工作目录
@@ -68,8 +67,8 @@ Uni-Lab 的启动过程分为以下几个阶段:
支持多种后端环境:
- `--addr test`:测试环境 (`https://uni-lab.test.bohrium.com/api/v1`)
- `--addr uat`UAT 环境 (`https://uni-lab.uat.bohrium.com/api/v1`)
- `--addr test`:测试环境 (`https://leap-lab.test.bohrium.com/api/v1`)
- `--addr uat`UAT 环境 (`https://leap-lab.uat.bohrium.com/api/v1`)
- `--addr local`:本地环境 (`http://127.0.0.1:48197/api/v1`)
- 自定义地址:直接指定完整 URL
@@ -176,7 +175,7 @@ unilab --config path/to/your/config.py
如果是首次使用,系统会:
1. 提示前往 https://uni-lab.bohrium.com 注册实验室
1. 提示前往 https://leap-lab.bohrium.com 注册实验室
2. 引导创建配置文件
3. 设置工作目录
@@ -216,7 +215,7 @@ unilab --ak your_ak --sk your_sk --port 8080 --disable_browser
如果提示 "后续运行必须拥有一个实验室",请确保:
- 已在 https://uni-lab.bohrium.com 注册实验室
- 已在 https://leap-lab.bohrium.com 注册实验室
- 正确设置了 `--ak``--sk` 参数
- 配置文件中包含正确的认证信息

View File

@@ -1,6 +1,6 @@
package:
name: ros-humble-unilabos-msgs
version: 0.10.19
version: 0.11.1
source:
path: ../../unilabos_msgs
target_directory: src

View File

@@ -1,6 +1,6 @@
package:
name: unilabos
version: "0.10.19"
version: "0.11.1"
source:
path: ../..

View File

@@ -2,6 +2,7 @@ import json
import logging
import traceback
import uuid
import xml.etree.ElementTree as ET
from typing import Any, Dict, List
import networkx as nx
@@ -24,15 +25,7 @@ class SimpleGraph:
def add_edge(self, source, target, **attrs):
"""添加边"""
# edge = {"source": source, "target": target, **attrs}
edge = {
"source": source, "target": target,
"source_node_uuid": source,
"target_node_uuid": target,
"source_handle_io": "source",
"target_handle_io": "target",
**attrs
}
edge = {"source": source, "target": target, **attrs}
self.edges.append(edge)
def to_dict(self):
@@ -49,7 +42,6 @@ class SimpleGraph:
"multigraph": False,
"graph": {},
"nodes": nodes_list,
"edges": self.edges,
"links": self.edges,
}
@@ -66,8 +58,495 @@ def extract_json_from_markdown(text: str) -> str:
return text
def convert_to_type(val: str) -> Any:
"""将字符串值转换为适当的数据类型"""
if val == "True":
return True
if val == "False":
return False
if val == "?":
return None
if val.endswith(" g"):
return float(val.split(" ")[0])
if val.endswith("mg"):
return float(val.split("mg")[0])
elif val.endswith("mmol"):
return float(val.split("mmol")[0]) / 1000
elif val.endswith("mol"):
return float(val.split("mol")[0])
elif val.endswith("ml"):
return float(val.split("ml")[0])
elif val.endswith("RPM"):
return float(val.split("RPM")[0])
elif val.endswith(" °C"):
return float(val.split(" ")[0])
elif val.endswith(" %"):
return float(val.split(" ")[0])
return val
def refactor_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""统一的数据重构函数,根据操作类型自动选择模板"""
refactored_data = []
# 定义操作映射,包含生物实验和有机化学的所有操作
OPERATION_MAPPING = {
# 生物实验操作
"transfer_liquid": "SynBioFactory-liquid_handler.prcxi-transfer_liquid",
"transfer": "SynBioFactory-liquid_handler.biomek-transfer",
"incubation": "SynBioFactory-liquid_handler.biomek-incubation",
"move_labware": "SynBioFactory-liquid_handler.biomek-move_labware",
"oscillation": "SynBioFactory-liquid_handler.biomek-oscillation",
# 有机化学操作
"HeatChillToTemp": "SynBioFactory-workstation-HeatChillProtocol",
"StopHeatChill": "SynBioFactory-workstation-HeatChillStopProtocol",
"StartHeatChill": "SynBioFactory-workstation-HeatChillStartProtocol",
"HeatChill": "SynBioFactory-workstation-HeatChillProtocol",
"Dissolve": "SynBioFactory-workstation-DissolveProtocol",
"Transfer": "SynBioFactory-workstation-TransferProtocol",
"Evaporate": "SynBioFactory-workstation-EvaporateProtocol",
"Recrystallize": "SynBioFactory-workstation-RecrystallizeProtocol",
"Filter": "SynBioFactory-workstation-FilterProtocol",
"Dry": "SynBioFactory-workstation-DryProtocol",
"Add": "SynBioFactory-workstation-AddProtocol",
}
UNSUPPORTED_OPERATIONS = ["Purge", "Wait", "Stir", "ResetHandling"]
for step in data:
operation = step.get("action")
if not operation or operation in UNSUPPORTED_OPERATIONS:
continue
# 处理重复操作
if operation == "Repeat":
times = step.get("times", step.get("parameters", {}).get("times", 1))
sub_steps = step.get("steps", step.get("parameters", {}).get("steps", []))
for i in range(int(times)):
sub_data = refactor_data(sub_steps)
refactored_data.extend(sub_data)
continue
# 获取模板名称
template = OPERATION_MAPPING.get(operation)
if not template:
# 自动推断模板类型
if operation.lower() in ["transfer", "incubation", "move_labware", "oscillation"]:
template = f"SynBioFactory-liquid_handler.biomek-{operation}"
else:
template = f"SynBioFactory-workstation-{operation}Protocol"
# 创建步骤数据
step_data = {
"template": template,
"description": step.get("description", step.get("purpose", f"{operation} operation")),
"lab_node_type": "Device",
"parameters": step.get("parameters", step.get("action_args", {})),
}
refactored_data.append(step_data)
return refactored_data
def build_protocol_graph(
labware_info: List[Dict[str, Any]], protocol_steps: List[Dict[str, Any]], workstation_name: str
) -> SimpleGraph:
"""统一的协议图构建函数,根据设备类型自动选择构建逻辑"""
G = SimpleGraph()
resource_last_writer = {}
LAB_NAME = "SynBioFactory"
protocol_steps = refactor_data(protocol_steps)
# 检查协议步骤中的模板来判断协议类型
has_biomek_template = any(
("biomek" in step.get("template", "")) or ("prcxi" in step.get("template", ""))
for step in protocol_steps
)
if has_biomek_template:
# 生物实验协议图构建
for labware_id, labware in labware_info.items():
node_id = str(uuid.uuid4())
labware_attrs = labware.copy()
labware_id = labware_attrs.pop("id", labware_attrs.get("name", f"labware_{uuid.uuid4()}"))
labware_attrs["description"] = labware_id
labware_attrs["lab_node_type"] = (
"Reagent" if "Plate" in str(labware_id) else "Labware" if "Rack" in str(labware_id) else "Sample"
)
labware_attrs["device_id"] = workstation_name
G.add_node(node_id, template=f"{LAB_NAME}-host_node-create_resource", **labware_attrs)
resource_last_writer[labware_id] = f"{node_id}:labware"
# 处理协议步骤
prev_node = None
for i, step in enumerate(protocol_steps):
node_id = str(uuid.uuid4())
G.add_node(node_id, **step)
# 添加控制流边
if prev_node is not None:
G.add_edge(prev_node, node_id, source_port="ready", target_port="ready")
prev_node = node_id
# 处理物料流
params = step.get("parameters", {})
if "sources" in params and params["sources"] in resource_last_writer:
source_node, source_port = resource_last_writer[params["sources"]].split(":")
G.add_edge(source_node, node_id, source_port=source_port, target_port="labware")
if "targets" in params:
resource_last_writer[params["targets"]] = f"{node_id}:labware"
# 添加协议结束节点
end_id = str(uuid.uuid4())
G.add_node(end_id, template=f"{LAB_NAME}-liquid_handler.biomek-run_protocol")
if prev_node is not None:
G.add_edge(prev_node, end_id, source_port="ready", target_port="ready")
else:
# 有机化学协议图构建
WORKSTATION_ID = workstation_name
# 为所有labware创建资源节点
for item_id, item in labware_info.items():
# item_id = item.get("id") or item.get("name", f"item_{uuid.uuid4()}")
node_id = str(uuid.uuid4())
# 判断节点类型
if item.get("type") == "hardware" or "reactor" in str(item_id).lower():
if "reactor" not in str(item_id).lower():
continue
lab_node_type = "Sample"
description = f"Prepare Reactor: {item_id}"
liquid_type = []
liquid_volume = []
else:
lab_node_type = "Reagent"
description = f"Add Reagent to Flask: {item_id}"
liquid_type = [item_id]
liquid_volume = [1e5]
G.add_node(
node_id,
template=f"{LAB_NAME}-host_node-create_resource",
description=description,
lab_node_type=lab_node_type,
res_id=item_id,
device_id=WORKSTATION_ID,
class_name="container",
parent=WORKSTATION_ID,
bind_locations={"x": 0.0, "y": 0.0, "z": 0.0},
liquid_input_slot=[-1],
liquid_type=liquid_type,
liquid_volume=liquid_volume,
slot_on_deck="",
role=item.get("role", ""),
)
resource_last_writer[item_id] = f"{node_id}:labware"
last_control_node_id = None
# 处理协议步骤
for step in protocol_steps:
node_id = str(uuid.uuid4())
G.add_node(node_id, **step)
# 控制流
if last_control_node_id is not None:
G.add_edge(last_control_node_id, node_id, source_port="ready", target_port="ready")
last_control_node_id = node_id
# 物料流
params = step.get("parameters", {})
input_resources = {
"Vessel": params.get("vessel"),
"ToVessel": params.get("to_vessel"),
"FromVessel": params.get("from_vessel"),
"reagent": params.get("reagent"),
"solvent": params.get("solvent"),
"compound": params.get("compound"),
"sources": params.get("sources"),
"targets": params.get("targets"),
}
for target_port, resource_name in input_resources.items():
if resource_name and resource_name in resource_last_writer:
source_node, source_port = resource_last_writer[resource_name].split(":")
G.add_edge(source_node, node_id, source_port=source_port, target_port=target_port)
output_resources = {
"VesselOut": params.get("vessel"),
"FromVesselOut": params.get("from_vessel"),
"ToVesselOut": params.get("to_vessel"),
"FiltrateOut": params.get("filtrate_vessel"),
"reagent": params.get("reagent"),
"solvent": params.get("solvent"),
"compound": params.get("compound"),
"sources_out": params.get("sources"),
"targets_out": params.get("targets"),
}
for source_port, resource_name in output_resources.items():
if resource_name:
resource_last_writer[resource_name] = f"{node_id}:{source_port}"
return G
def draw_protocol_graph(protocol_graph: SimpleGraph, output_path: str):
"""
(辅助功能) 使用 networkx 和 matplotlib 绘制协议工作流图,用于可视化。
"""
if not protocol_graph:
print("Cannot draw graph: Graph object is empty.")
return
G = nx.DiGraph()
for node_id, attrs in protocol_graph.nodes.items():
label = attrs.get("description", attrs.get("template", node_id[:8]))
G.add_node(node_id, label=label, **attrs)
for edge in protocol_graph.edges:
G.add_edge(edge["source"], edge["target"])
plt.figure(figsize=(20, 15))
try:
pos = nx.nx_agraph.graphviz_layout(G, prog="dot")
except Exception:
pos = nx.shell_layout(G) # Fallback layout
node_labels = {node: data["label"] for node, data in G.nodes(data=True)}
nx.draw(
G,
pos,
with_labels=False,
node_size=2500,
node_color="skyblue",
node_shape="o",
edge_color="gray",
width=1.5,
arrowsize=15,
)
nx.draw_networkx_labels(G, pos, labels=node_labels, font_size=8, font_weight="bold")
plt.title("Chemical Protocol Workflow Graph", size=15)
plt.savefig(output_path, dpi=300, bbox_inches="tight")
plt.close()
print(f" - Visualization saved to '{output_path}'")
from networkx.drawing.nx_agraph import to_agraph
import re
COMPASS = {"n","e","s","w","ne","nw","se","sw","c"}
def _is_compass(port: str) -> bool:
return isinstance(port, str) and port.lower() in COMPASS
def draw_protocol_graph_with_ports(protocol_graph, output_path: str, rankdir: str = "LR"):
"""
使用 Graphviz 端口语法绘制协议工作流图。
- 若边上的 source_port/target_port 是 compassn/e/s/w/...),直接用 compass。
- 否则自动为节点创建 record 形状并定义命名端口 <portname>。
最终由 PyGraphviz 渲染并输出到 output_path后缀决定格式如 .png/.svg/.pdf
"""
if not protocol_graph:
print("Cannot draw graph: Graph object is empty.")
return
# 1) 先用 networkx 搭建有向图,保留端口属性
G = nx.DiGraph()
for node_id, attrs in protocol_graph.nodes.items():
label = attrs.get("description", attrs.get("template", node_id[:8]))
# 保留一个干净的“中心标签”,用于放在 record 的中间槽
G.add_node(node_id, _core_label=str(label), **{k:v for k,v in attrs.items() if k not in ("label",)})
edges_data = []
in_ports_by_node = {} # 收集命名输入端口
out_ports_by_node = {} # 收集命名输出端口
for edge in protocol_graph.edges:
u = edge["source"]
v = edge["target"]
sp = edge.get("source_port")
tp = edge.get("target_port")
# 记录到图里(保留原始端口信息)
G.add_edge(u, v, source_port=sp, target_port=tp)
edges_data.append((u, v, sp, tp))
# 如果不是 compass就按“命名端口”先归类等会儿给节点造 record
if sp and not _is_compass(sp):
out_ports_by_node.setdefault(u, set()).add(str(sp))
if tp and not _is_compass(tp):
in_ports_by_node.setdefault(v, set()).add(str(tp))
# 2) 转为 AGraph使用 Graphviz 渲染
A = to_agraph(G)
A.graph_attr.update(rankdir=rankdir, splines="true", concentrate="false", fontsize="10")
A.node_attr.update(shape="box", style="rounded,filled", fillcolor="lightyellow", color="#999999", fontname="Helvetica")
A.edge_attr.update(arrowsize="0.8", color="#666666")
# 3) 为需要命名端口的节点设置 record 形状与 label
# 左列 = 输入端口;中间 = 核心标签;右列 = 输出端口
for n in A.nodes():
node = A.get_node(n)
core = G.nodes[n].get("_core_label", n)
in_ports = sorted(in_ports_by_node.get(n, []))
out_ports = sorted(out_ports_by_node.get(n, []))
# 如果该节点涉及命名端口,则用 record否则保留原 box
if in_ports or out_ports:
def port_fields(ports):
if not ports:
return " " # 必须留一个空槽占位
# 每个端口一个小格子,<p> name
return "|".join(f"<{re.sub(r'[^A-Za-z0-9_:.|-]', '_', p)}> {p}" for p in ports)
left = port_fields(in_ports)
right = port_fields(out_ports)
# 三栏:左(入) | 中(节点名) | 右(出)
record_label = f"{{ {left} | {core} | {right} }}"
node.attr.update(shape="record", label=record_label)
else:
# 没有命名端口:普通盒子,显示核心标签
node.attr.update(label=str(core))
# 4) 给边设置 headport / tailport
# - 若端口为 compass直接用 compasse.g., headport="e"
# - 若端口为命名端口:使用在 record 中定义的 <port> 名(同名即可)
for (u, v, sp, tp) in edges_data:
e = A.get_edge(u, v)
# Graphviz 属性tail 是源head 是目标
if sp:
if _is_compass(sp):
e.attr["tailport"] = sp.lower()
else:
# 与 record label 中 <port> 名一致;特殊字符已在 label 中做了清洗
e.attr["tailport"] = re.sub(r'[^A-Za-z0-9_:.|-]', '_', str(sp))
if tp:
if _is_compass(tp):
e.attr["headport"] = tp.lower()
else:
e.attr["headport"] = re.sub(r'[^A-Za-z0-9_:.|-]', '_', str(tp))
# 可选:若想让边更贴边缘,可设置 constraint/spline 等
# e.attr["arrowhead"] = "vee"
# 5) 输出
A.draw(output_path, prog="dot")
print(f" - Port-aware workflow rendered to '{output_path}'")
def flatten_xdl_procedure(procedure_elem: ET.Element) -> List[ET.Element]:
"""展平嵌套的XDL程序结构"""
flattened_operations = []
TEMP_UNSUPPORTED_PROTOCOL = ["Purge", "Wait", "Stir", "ResetHandling"]
def extract_operations(element: ET.Element):
if element.tag not in ["Prep", "Reaction", "Workup", "Purification", "Procedure"]:
if element.tag not in TEMP_UNSUPPORTED_PROTOCOL:
flattened_operations.append(element)
for child in element:
extract_operations(child)
for child in procedure_elem:
extract_operations(child)
return flattened_operations
def parse_xdl_content(xdl_content: str) -> tuple:
"""解析XDL内容"""
try:
xdl_content_cleaned = "".join(c for c in xdl_content if c.isprintable())
root = ET.fromstring(xdl_content_cleaned)
synthesis_elem = root.find("Synthesis")
if synthesis_elem is None:
return None, None, None
# 解析硬件组件
hardware_elem = synthesis_elem.find("Hardware")
hardware = []
if hardware_elem is not None:
hardware = [{"id": c.get("id"), "type": c.get("type")} for c in hardware_elem.findall("Component")]
# 解析试剂
reagents_elem = synthesis_elem.find("Reagents")
reagents = []
if reagents_elem is not None:
reagents = [{"name": r.get("name"), "role": r.get("role", "")} for r in reagents_elem.findall("Reagent")]
# 解析程序
procedure_elem = synthesis_elem.find("Procedure")
if procedure_elem is None:
return None, None, None
flattened_operations = flatten_xdl_procedure(procedure_elem)
return hardware, reagents, flattened_operations
except ET.ParseError as e:
raise ValueError(f"Invalid XDL format: {e}")
def convert_xdl_to_dict(xdl_content: str) -> Dict[str, Any]:
"""
将XDL XML格式转换为标准的字典格式
Args:
xdl_content: XDL XML内容
Returns:
转换结果,包含步骤和器材信息
"""
try:
hardware, reagents, flattened_operations = parse_xdl_content(xdl_content)
if hardware is None:
return {"error": "Failed to parse XDL content", "success": False}
# 将XDL元素转换为字典格式
steps_data = []
for elem in flattened_operations:
# 转换参数类型
parameters = {}
for key, val in elem.attrib.items():
converted_val = convert_to_type(val)
if converted_val is not None:
parameters[key] = converted_val
step_dict = {
"operation": elem.tag,
"parameters": parameters,
"description": elem.get("purpose", f"Operation: {elem.tag}"),
}
steps_data.append(step_dict)
# 合并硬件和试剂为统一的labware_info格式
labware_data = []
labware_data.extend({"id": hw["id"], "type": "hardware", **hw} for hw in hardware)
labware_data.extend({"name": reagent["name"], "type": "reagent", **reagent} for reagent in reagents)
return {
"success": True,
"steps": steps_data,
"labware": labware_data,
"message": f"Successfully converted XDL to dict format. Found {len(steps_data)} steps and {len(labware_data)} labware items.",
}
except Exception as e:
error_msg = f"XDL conversion failed: {str(e)}"
logger.error(error_msg)
return {"error": error_msg, "success": False}
def create_workflow(

View File

@@ -4,7 +4,7 @@ package_name = 'unilabos'
setup(
name=package_name,
version='0.10.19',
version='0.11.1',
packages=find_packages(),
include_package_data=True,
install_requires=['setuptools'],

View File

@@ -1 +1 @@
__version__ = "0.10.19"
__version__ = "0.11.1"

View File

@@ -12,6 +12,15 @@ from typing import Dict, Any, List
import networkx as nx
import yaml
# Windows 中文系统 stdout 默认 GBK无法编码 banner / emoji 日志中的 Unicode 字符
# 强制 stdout/stderr 用 UTF-8避免 print 触发 UnicodeEncodeError 导致进程崩溃
if sys.platform == "win32":
for _stream in (sys.stdout, sys.stderr):
try:
_stream.reconfigure(encoding="utf-8", errors="replace") # type: ignore[attr-defined]
except (AttributeError, OSError):
pass
# 首先添加项目根目录到路径
current_dir = os.path.dirname(os.path.abspath(__file__))
unilabos_dir = os.path.dirname(os.path.dirname(current_dir))
@@ -233,7 +242,7 @@ def parse_args():
parser.add_argument(
"--addr",
type=str,
default="https://uni-lab.bohrium.com/api/v1",
default="https://leap-lab.bohrium.com/api/v1",
help="Laboratory backend address",
)
parser.add_argument(
@@ -438,10 +447,10 @@ def main():
if args.addr != parser.get_default("addr"):
if args.addr == "test":
print_status("使用测试环境地址", "info")
HTTPConfig.remote_addr = "https://uni-lab.test.bohrium.com/api/v1"
HTTPConfig.remote_addr = "https://leap-lab.test.bohrium.com/api/v1"
elif args.addr == "uat":
print_status("使用uat环境地址", "info")
HTTPConfig.remote_addr = "https://uni-lab.uat.bohrium.com/api/v1"
HTTPConfig.remote_addr = "https://leap-lab.uat.bohrium.com/api/v1"
elif args.addr == "local":
print_status("使用本地环境地址", "info")
HTTPConfig.remote_addr = "http://127.0.0.1:48197/api/v1"
@@ -553,7 +562,7 @@ def main():
os._exit(0)
if not BasicConfig.ak or not BasicConfig.sk:
print_status("后续运行必须拥有一个实验室,请前往 https://uni-lab.bohrium.com 注册实验室!", "warning")
print_status("后续运行必须拥有一个实验室,请前往 https://leap-lab.bohrium.com 注册实验室!", "warning")
os._exit(1)
graph: nx.Graph
resource_tree_set: ResourceTreeSet

View File

@@ -36,6 +36,9 @@ class HTTPClient:
auth_secret = BasicConfig.auth_secret()
self.auth = auth_secret
info(f"正在使用ak sk作为授权信息[{auth_secret}]")
# 复用 TCP/TLS 连接,避免每次请求重新握手
self._session = requests.Session()
self._session.headers.update({"Authorization": f"Lab {self.auth}"})
info(f"HTTPClient 初始化完成: remote_addr={self.remote_addr}")
def resource_edge_add(self, resources: List[Dict[str, Any]]) -> requests.Response:
@@ -48,7 +51,7 @@ class HTTPClient:
Returns:
Response: API响应对象
"""
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/edge/material/edge",
json={
"edges": resources,
@@ -75,26 +78,28 @@ class HTTPClient:
Returns:
Dict[str, str]: 旧UUID到新UUID的映射关系 {old_uuid: new_uuid}
"""
with open(os.path.join(BasicConfig.working_dir, "req_resource_tree_add.json"), "w", encoding="utf-8") as f:
payload = {"nodes": [x for xs in resources.dump() for x in xs], "mount_uuid": mount_uuid}
f.write(json.dumps(payload, indent=4))
# 从序列化数据中提取所有节点的UUID保存旧UUID
old_uuids = {n.res_content.uuid: n for n in resources.all_nodes}
# dump() 只调用一次,复用给文件保存和 HTTP 请求
nodes_info = [x for xs in resources.dump() for x in xs]
old_uuids = {n.res_content.uuid: n for n in resources.all_nodes}
payload = {"nodes": nodes_info, "mount_uuid": mount_uuid}
body_bytes = _fast_dumps(payload)
with open(os.path.join(BasicConfig.working_dir, "req_resource_tree_add.json"), "wb") as f:
f.write(_fast_dumps_pretty(payload))
http_headers = {"Content-Type": "application/json"}
if not self.initialized or first_add:
self.initialized = True
info(f"首次添加资源,当前远程地址: {self.remote_addr}")
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/edge/material",
json={"nodes": nodes_info, "mount_uuid": mount_uuid},
headers={"Authorization": f"Lab {self.auth}"},
data=body_bytes,
headers=http_headers,
timeout=60,
)
else:
response = requests.put(
response = self._session.put(
f"{self.remote_addr}/edge/material",
json={"nodes": nodes_info, "mount_uuid": mount_uuid},
headers={"Authorization": f"Lab {self.auth}"},
data=body_bytes,
headers=http_headers,
timeout=10,
)
@@ -133,7 +138,7 @@ class HTTPClient:
"""
with open(os.path.join(BasicConfig.working_dir, "req_resource_tree_get.json"), "w", encoding="utf-8") as f:
f.write(json.dumps({"uuids": uuid_list, "with_children": with_children}, indent=4))
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/edge/material/query",
json={"uuids": uuid_list, "with_children": with_children},
headers={"Authorization": f"Lab {self.auth}"},
@@ -147,6 +152,7 @@ class HTTPClient:
logger.error(f"查询物料失败: {response.text}")
else:
data = res["data"]["nodes"]
logger.trace(f"resource_tree_get查询到物料: {data}")
return data
else:
logger.error(f"查询物料失败: {response.text}")
@@ -164,14 +170,14 @@ class HTTPClient:
if not self.initialized:
self.initialized = True
info(f"首次添加资源,当前远程地址: {self.remote_addr}")
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/lab/material",
json={"nodes": resources},
headers={"Authorization": f"Lab {self.auth}"},
timeout=100,
)
else:
response = requests.put(
response = self._session.put(
f"{self.remote_addr}/lab/material",
json={"nodes": resources},
headers={"Authorization": f"Lab {self.auth}"},
@@ -198,7 +204,7 @@ class HTTPClient:
"""
with open(os.path.join(BasicConfig.working_dir, "req_resource_get.json"), "w", encoding="utf-8") as f:
f.write(json.dumps({"id": id, "with_children": with_children}, indent=4))
response = requests.get(
response = self._session.get(
f"{self.remote_addr}/lab/material",
params={"id": id, "with_children": with_children},
headers={"Authorization": f"Lab {self.auth}"},
@@ -239,14 +245,14 @@ class HTTPClient:
if not self.initialized:
self.initialized = True
info(f"首次添加资源,当前远程地址: {self.remote_addr}")
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/lab/material",
json={"nodes": resources},
headers={"Authorization": f"Lab {self.auth}"},
timeout=100,
)
else:
response = requests.put(
response = self._session.put(
f"{self.remote_addr}/lab/material",
json={"nodes": resources},
headers={"Authorization": f"Lab {self.auth}"},
@@ -276,7 +282,7 @@ class HTTPClient:
with open(file_path, "rb") as file:
files = {"files": file}
logger.info(f"上传文件: {file_path}{scene}")
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/api/account/file_upload/{scene}",
files=files,
headers={"Authorization": f"Lab {self.auth}"},
@@ -316,7 +322,7 @@ class HTTPClient:
"Content-Type": "application/json",
"Content-Encoding": "gzip",
}
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/lab/resource",
data=compressed_body,
headers=headers,
@@ -350,7 +356,7 @@ class HTTPClient:
Returns:
Response: API响应对象
"""
response = requests.get(
response = self._session.get(
f"{self.remote_addr}/edge/material/download",
headers={"Authorization": f"Lab {self.auth}"},
timeout=(3, 30),
@@ -411,7 +417,7 @@ class HTTPClient:
with open(os.path.join(BasicConfig.working_dir, "req_workflow_upload.json"), "w", encoding="utf-8") as f:
f.write(json.dumps(payload, indent=4, ensure_ascii=False))
response = requests.post(
response = self._session.post(
f"{self.remote_addr}/lab/workflow/owner/import",
json=payload,
headers={"Authorization": f"Lab {self.auth}"},

View File

@@ -1269,7 +1269,13 @@ class QueueProcessor:
if not queued_jobs:
return
logger.debug(f"[QueueProcessor] Sending busy status for {len(queued_jobs)} queued jobs")
queue_summary = {}
for j in queued_jobs:
key = f"{j.device_id}/{j.action_name}"
queue_summary[key] = queue_summary.get(key, 0) + 1
logger.debug(
f"[QueueProcessor] Sending busy status for {len(queued_jobs)} queued jobs: {queue_summary}"
)
for job_info in queued_jobs:
# 快照可能已过期:在遍历过程中 end_job() 可能已将此 job 移至 READY

View File

@@ -46,7 +46,7 @@ class WSConfig:
# HTTP配置
class HTTPConfig:
remote_addr = "https://uni-lab.bohrium.com/api/v1"
remote_addr = "https://leap-lab.bohrium.com/api/v1"
# ROS配置

View File

@@ -2,6 +2,8 @@ import time
import logging
from typing import Union, Dict, Optional
from unilabos.registry.decorators import topic_config
class VirtualMultiwayValve:
"""
@@ -41,13 +43,11 @@ class VirtualMultiwayValve:
def target_position(self) -> int:
return self._target_position
def get_current_position(self) -> int:
"""获取当前阀门位置 📍"""
return self._current_position
def get_current_port(self) -> str:
"""获取当前连接的端口名称 🔌"""
return self._current_position
@property
@topic_config()
def current_port(self) -> str:
"""当前连接的端口名称 🔌"""
return self.port
def set_position(self, command: Union[int, str]):
"""
@@ -169,12 +169,14 @@ class VirtualMultiwayValve:
self._status = "Idle"
self._valve_state = "Closed"
close_msg = f"🔒 阀门已关闭,保持在位置 {self._current_position} ({self.get_current_port()})"
close_msg = f"🔒 阀门已关闭,保持在位置 {self._current_position} ({self.port})"
self.logger.info(close_msg)
return close_msg
def get_valve_position(self) -> int:
"""获取阀门位置 - 兼容性方法 📍"""
@property
@topic_config()
def valve_position(self) -> int:
"""阀门位置 📍"""
return self._current_position
def set_valve_position(self, command: Union[int, str]):
@@ -229,19 +231,16 @@ class VirtualMultiwayValve:
self.logger.info(f"🔄 从端口 {self._current_position} 切换到泵位置...")
return self.set_to_pump_position()
def get_flow_path(self) -> str:
"""获取当前流路路径描述 🌊"""
current_port = self.get_current_port()
@property
@topic_config()
def flow_path(self) -> str:
"""当前流路路径描述 🌊"""
if self._current_position == 0:
flow_path = f"🚰 转移泵已连接 (位置 {self._current_position})"
else:
flow_path = f"🔌 端口 {self._current_position} 已连接 ({current_port})"
# 删除debug日志self.logger.debug(f"🌊 当前流路: {flow_path}")
return flow_path
return f"🚰 转移泵已连接 (位置 {self._current_position})"
return f"🔌 端口 {self._current_position} 已连接 ({self.current_port})"
def __str__(self):
current_port = self.get_current_port()
current_port = self.current_port
status_emoji = "" if self._status == "Idle" else "🔄" if self._status == "Busy" else ""
return f"🔄 VirtualMultiwayValve({status_emoji} 位置: {self._current_position}/{self.max_positions}, 端口: {current_port}, 状态: {self._status})"
@@ -253,7 +252,7 @@ if __name__ == "__main__":
print("🔄 === 虚拟九通阀门测试 === ✨")
print(f"🏠 初始状态: {valve}")
print(f"🌊 当前流路: {valve.get_flow_path()}")
print(f"🌊 当前流路: {valve.flow_path}")
# 切换到试剂瓶11号位
print(f"\n🔌 切换到1号位: {valve.set_position(1)}")

View File

@@ -3,6 +3,7 @@ import logging
import time as time_module
from typing import Dict, Any
from unilabos.registry.decorators import topic_config
from unilabos.ros.nodes.base_device_node import BaseROS2DeviceNode
class VirtualStirrer:
@@ -314,9 +315,11 @@ class VirtualStirrer:
def min_speed(self) -> float:
return self._min_speed
def get_device_info(self) -> Dict[str, Any]:
"""获取设备状态信息 📊"""
info = {
@property
@topic_config()
def device_info(self) -> Dict[str, Any]:
"""设备状态快照信息 📊"""
return {
"device_id": self.device_id,
"status": self.status,
"operation_mode": self.operation_mode,
@@ -325,12 +328,9 @@ class VirtualStirrer:
"is_stirring": self.is_stirring,
"remaining_time": self.remaining_time,
"max_speed": self._max_speed,
"min_speed": self._min_speed
"min_speed": self._min_speed,
}
# self.logger.debug(f"📊 设备信息: 模式={self.operation_mode}, 速度={self.current_speed} RPM, 搅拌={self.is_stirring}")
return info
def __str__(self):
status_emoji = "" if self.operation_mode == "Idle" else "🌪️" if self.operation_mode == "Stirring" else "🛑" if self.operation_mode == "Settling" else ""
return f"🌪️ VirtualStirrer({status_emoji} {self.device_id}: {self.operation_mode}, {self.current_speed} RPM)"

View File

@@ -4,6 +4,7 @@ from enum import Enum
from typing import Union, Optional
import logging
from unilabos.registry.decorators import topic_config
from unilabos.ros.nodes.base_device_node import BaseROS2DeviceNode
@@ -385,8 +386,10 @@ class VirtualTransferPump:
"""获取当前体积"""
return self._current_volume
def get_remaining_capacity(self) -> float:
"""获取剩余容量"""
@property
@topic_config()
def remaining_capacity(self) -> float:
"""剩余容量 (ml)"""
return self.max_volume - self._current_volume
def is_empty(self) -> bool:

View File

@@ -22,10 +22,11 @@ from threading import Lock, RLock
from typing_extensions import TypedDict
from unilabos.registry.decorators import (
device, action, ActionInputHandle, ActionOutputHandle, DataSource, topic_config, not_action
device, action, ActionInputHandle, ActionOutputHandle, DataSource, topic_config, not_action, NodeType
)
from unilabos.ros.nodes.base_device_node import BaseROS2DeviceNode
from unilabos.resources.resource_tracker import SampleUUIDsType, LabSample
from unilabos.registry.placeholder_type import ResourceSlot, DeviceSlot
from unilabos.ros.nodes.base_device_node import BaseROS2DeviceNode, ROS2DeviceNode
from unilabos.resources.resource_tracker import SampleUUIDsType, LabSample, ResourceTreeSet
# ============ TypedDict 返回类型定义 ============
@@ -290,6 +291,126 @@ class VirtualWorkbench:
self._update_data_status(f"机械臂已释放 (完成: {task})")
self.logger.info(f"机械臂已释放 (完成: {task})")
@action(
always_free=True, node_type=NodeType.MANUAL_CONFIRM, placeholder_keys={
"assignee_user_ids": "unilabos_manual_confirm"
}, goal_default={
"timeout_seconds": 3600,
"assignee_user_ids": []
}, feedback_interval=300,
handles=[
ActionInputHandle(key="target_device", data_type="device_id",
label="目标设备", data_key="target_device", data_source=DataSource.HANDLE),
ActionInputHandle(key="resource", data_type="resource",
label="待转移资源", data_key="resource", data_source=DataSource.HANDLE),
ActionInputHandle(key="mount_resource", data_type="resource",
label="目标孔位", data_key="mount_resource", data_source=DataSource.HANDLE),
ActionInputHandle(key="collector_mass", data_type="collector_mass",
label="极流体质量", data_key="collector_mass", data_source=DataSource.HANDLE),
ActionInputHandle(key="active_material", data_type="active_material",
label="活性物质含量", data_key="active_material", data_source=DataSource.HANDLE),
ActionInputHandle(key="capacity", data_type="capacity",
label="克容量", data_key="capacity", data_source=DataSource.HANDLE),
ActionInputHandle(key="battery_system", data_type="battery_system",
label="电池体系", data_key="battery_system", data_source=DataSource.HANDLE),
# transfer使用
ActionOutputHandle(key="target_device", data_type="device_id",
label="目标设备", data_key="target_device", data_source=DataSource.EXECUTOR),
ActionOutputHandle(key="resource", data_type="resource",
label="待转移资源", data_key="resource.@flatten", data_source=DataSource.EXECUTOR),
ActionOutputHandle(key="mount_resource", data_type="resource",
label="目标孔位", data_key="mount_resource.@flatten", data_source=DataSource.EXECUTOR),
# test使用
ActionOutputHandle(key="collector_mass", data_type="collector_mass",
label="极流体质量", data_key="collector_mass", data_source=DataSource.EXECUTOR),
ActionOutputHandle(key="active_material", data_type="active_material",
label="活性物质含量", data_key="active_material", data_source=DataSource.EXECUTOR),
ActionOutputHandle(key="capacity", data_type="capacity",
label="克容量", data_key="capacity", data_source=DataSource.EXECUTOR),
ActionOutputHandle(key="battery_system", data_type="battery_system",
label="电池体系", data_key="battery_system", data_source=DataSource.EXECUTOR),
]
)
def manual_confirm(
self,
resource: List[ResourceSlot],
target_device: DeviceSlot,
mount_resource: List[ResourceSlot],
collector_mass: List[float],
active_material: List[float],
capacity: List[float],
battery_system: List[str],
timeout_seconds: int,
assignee_user_ids: list[str],
**kwargs
) -> dict:
"""
timeout_seconds: 超时时间默认3600秒
collector_mass: 极流体质量
active_material: 活性物质含量
capacity: 克容量mAh/g
battery_system: 电池体系
修改的结果无效,是只读的
"""
resource = ResourceTreeSet.from_plr_resources(resource).dump()
mount_resource = ResourceTreeSet.from_plr_resources(mount_resource).dump()
kwargs.update(locals())
kwargs.pop("kwargs")
kwargs.pop("self")
return kwargs
@action(
description="转移物料",
handles=[
ActionInputHandle(key="target_device", data_type="device_id",
label="目标设备", data_key="target_device", data_source=DataSource.HANDLE),
ActionInputHandle(key="resource", data_type="resource",
label="待转移资源", data_key="resource", data_source=DataSource.HANDLE),
ActionInputHandle(key="mount_resource", data_type="resource",
label="目标孔位", data_key="mount_resource", data_source=DataSource.HANDLE),
]
)
async def transfer(self, resource: List[ResourceSlot], target_device: DeviceSlot, mount_resource: List[ResourceSlot]):
future = ROS2DeviceNode.run_async_func(self._ros_node.transfer_resource_to_another, True,
**{
"plr_resources": resource,
"target_device_id": target_device,
"target_resources": mount_resource,
"sites": [None] * len(mount_resource),
})
result = await future
return result
@action(
description="扣电测试启动",
handles=[
ActionInputHandle(key="resource", data_type="resource",
label="待转移资源", data_key="resource", data_source=DataSource.HANDLE),
ActionInputHandle(key="mount_resource", data_type="resource",
label="目标孔位", data_key="mount_resource", data_source=DataSource.HANDLE),
ActionInputHandle(key="collector_mass", data_type="collector_mass",
label="极流体质量", data_key="collector_mass", data_source=DataSource.HANDLE),
ActionInputHandle(key="active_material", data_type="active_material",
label="活性物质含量", data_key="active_material", data_source=DataSource.HANDLE),
ActionInputHandle(key="capacity", data_type="capacity",
label="克容量", data_key="capacity", data_source=DataSource.HANDLE),
ActionInputHandle(key="battery_system", data_type="battery_system",
label="电池体系", data_key="battery_system", data_source=DataSource.HANDLE),
]
)
async def test(
self, resource: List[ResourceSlot], mount_resource: List[ResourceSlot], collector_mass: List[float], active_material: List[float], capacity: List[float], battery_system: list[str]
):
print(resource)
print(mount_resource)
print(collector_mass)
print(active_material)
print(capacity)
print(battery_system)
@action(
auto_prefix=True,
description="批量准备物料 - 虚拟起始节点, 生成A1-A5物料, 输出5个handle供后续节点使用",

View File

@@ -1,634 +0,0 @@
# Layout Optimizer Handover
**Date**: 2026-04-10 | **Branch**: `feat/3d_layout_and_visualize` | **Commit**: `99dc821a` | **Tests**: 270 (260 pass + 10 LLM skip w/o API key)
This package is a standalone lab layout optimizer. It takes a device list + constraints and returns optimized placements. Your integration points are the HTTP API and the LLM skill document.
---
## 1. Full Pipeline Overview
```
User NL request
┌─────────────────┐ skill doc: llm_skill/layout_intent_translator.md
│ LLM Agent │◄── + device list from scene (GET /devices)
│ (your side) │ + schema discovery (GET /interpret/schema)
└────────┬────────┘
│ structured intents JSON
POST /interpret ← intent_interpreter.py (pure translation)
│ { constraints, translations, workflow_edges, errors }
User confirms ← translations have human-readable explanations
POST /optimize ← full pipeline below
┌────┴─────────────────────────────────────────┐
│ 1. Device catalog (device_catalog.py) │
│ footprints.json → Device objects │
│ bbox, height, openings per device │
│ │
│ 2. Seeder (seeders.py) │
│ Force-directed initial placement │
│ Presets: compact_outward, spread_inward, │
│ workflow_cluster, row_fallback │
│ Accounts for openings, workflow edges │
│ │
│ 3. DE Optimizer (optimizer.py) │
│ Custom DE loop (best1bin/currenttobest1bin│
│ /rand1bin strategies) │
│ 3N-dim: [x0, y0, θ0, x1, y1, θ1, ...] │
│ Broad-phase AABB sweep (broad_phase.py) │
│ θ lattice snap in joint discrete mode │
│ Cost = hard_penalties + soft_penalties │
│ Graduated collision penalties (not binary) │
│ │
│ 4. θ snap (optimizer.snap_theta) │
│ Snap near-cardinal angles to 0/90/180/270 │
│ (opt-in via snap_cardinal=True) │
│ │
│ 5. Final eval (constraints.py) │
│ Binary pass/fail for response.success │
└──────────────────────────────────────────────┘
{ placements, cost, success }
```
---
## 2. API Reference
### `POST /interpret` — LLM intent → constraints
Translates semantic intents into optimizer constraints. The LLM agent calls this after translating user NL.
**Request:**
```json
{
"intents": [
{
"intent": "reachable_by",
"params": {"arm": "arm_slider", "targets": ["opentrons_liquid_handler", "inheco_odtc_96xl"]},
"description": "Robot arm must reach these devices"
},
{
"intent": "workflow_hint",
"params": {"workflow": "pcr", "devices": ["device_a", "device_b", "device_c"]},
"description": "PCR workflow order"
},
{
"intent": "close_together",
"params": {"devices": ["device_a", "device_b"], "priority": "high"},
"description": "Keep these close"
}
]
}
```
**Response:**
```json
{
"constraints": [
{"type": "hard", "rule_name": "reachability", "params": {"arm_id": "arm_slider", "target_device_id": "opentrons_liquid_handler"}, "weight": 1.0},
...
],
"translations": [
{
"source_intent": "reachable_by",
"source_description": "Robot arm must reach these devices",
"source_params": {"arm": "arm_slider", "targets": ["..."]},
"generated_constraints": [...],
"explanation": "机械臂 'arm_slider' 需要能够到达 2 个目标设备",
"confidence": "high"
}
],
"workflow_edges": [["device_a", "device_b"], ["device_b", "device_c"]],
"errors": []
}
```
The `constraints` and `workflow_edges` arrays pass directly to `/optimize` — no transformation needed.
### `GET /interpret/schema` — LLM discovery
Returns all 11 intent types with parameter specs. LLM agent should call this before translating.
### `POST /optimize` — Run layout optimization
**Request:**
```json
{
"devices": [
{"id": "thermo_orbitor_rs2_hotel", "name": "Plate Hotel", "device_type": "static"},
{"id": "arm_slider", "name": "Robot Arm", "device_type": "articulation"},
...
],
"lab": {"width": 6.0, "depth": 4.0},
"constraints": [...],
"workflow_edges": [["device_a", "device_b"]],
"seeder": "compact_outward",
"run_de": true,
"maxiter": 200,
"seed": 42,
"angle_granularity": 4,
"snap_cardinal": false,
"strategy": "currenttobest1bin",
"mutation": [0.5, 1.0],
"theta_mutation": null,
"recombination": 0.7,
"crossover_mode": "device"
}
```
**Response:**
```json
{
"placements": [
{
"device_id": "thermo_orbitor_rs2_hotel",
"uuid": "thermo_orbitor_rs2_hotel",
"position": {"x": 1.33, "y": 2.35, "z": 0.0},
"rotation": {"x": 0.0, "y": 0.0, "z": 1.5708}
},
...
],
"cost": 0.0,
"success": true,
"seeder_used": "compact_outward",
"de_ran": true
}
```
`position`/`rotation` format matches Cloud's `CommonPositionType`. `rotation.z` is θ in radians.
**DE hyperparameters:**
| Param | Default | Description |
|-------|---------|-------------|
| `strategy` | `"currenttobest1bin"` | DE mutation strategy (`best1bin`, `currenttobest1bin`, `rand1bin`) |
| `mutation` | `[0.5, 1.0]` | Dithered F range for position dimensions |
| `theta_mutation` | `null` (same as `mutation`) | Separate F range for θ dimensions (decoupled mutation) |
| `recombination` | `0.7` | Crossover probability |
| `crossover_mode` | `"device"` | `"device"` = per-device CR, `"dimension"` = per-dimension CR |
| `angle_granularity` | `null` | `4`/`8`/`12`/`24` — snaps θ to a discrete lattice during DE (joint mode). `4` = axis-aligned (0/90/180/270). `null` = continuous θ |
| `snap_cardinal` | `false` | Post-DE snap to nearest cardinal angle with collision rollback |
### Scene State API
Shared scene state between the LLM agent and the frontend. The agent pushes layout results here; the frontend polls for updates.
#### `GET /scene/lab` / `POST /scene/lab` — Lab dimensions
**GET** returns current lab dimensions. **POST** sets them (frontend sends this when user changes lab size).
```json
{"width": 6.0, "depth": 4.0}
```
#### `GET /scene/placements` / `POST /scene/placements` / `DELETE /scene/placements`
**GET** returns current placements + a version counter. Frontend polls this every 1s and re-renders when version changes.
```json
{"version": 3, "placements": [...]}
```
**POST** pushes new placements (from `/optimize` result or agent). Bumps version.
**DELETE** clears all placements (resets scene).
### `GET /devices` — Device catalog
Returns all known devices with bbox, openings, model paths. The LLM agent should receive this list as context so it can resolve fuzzy device names.
### `GET /health`
Returns `{"status": "ok"}`.
---
## 3. Intent Types (11 total)
| Intent | Params | Generates | Type |
|--------|--------|-----------|------|
| `reachable_by` | `arm` (str), `targets` (list[str]) | `reachability` per target | hard |
| `close_together` | `devices` (list[str]), `priority` (low/medium/high) | `minimize_distance` per pair | soft |
| `far_apart` | `devices` (list[str]), `priority` | `maximize_distance` per pair | soft |
| `keep_adjacent` | `devices` (list[str]), `priority` | `minimize_distance` per pair | soft |
| `max_distance` | `device_a`, `device_b`, `distance` (float m) | `distance_less_than` | hard |
| `min_distance` | `device_a`, `device_b`, `distance` (float m) | `distance_greater_than` | hard |
| `min_spacing` | `min_gap` (float m, default 0.3) | `min_spacing` | hard |
| `workflow_hint` | `workflow` (str), `devices` (ordered list[str]) | `minimize_distance` consecutive + `workflow_edges` | soft |
| `face_outward` | (none) | `prefer_orientation_mode` outward | soft |
| `face_inward` | (none) | `prefer_orientation_mode` inward | soft |
| `align_cardinal` | (none) | `prefer_aligned` | soft |
Intent priorities are baked into the final emitted constraint `weight` during interpretation. The caller only sees the resulting weight, not a separate constraint-level priority field.
---
## 4. LLM Integration Guide
### What You Need to Build (Your Side)
The LLM agent that converts user natural language → structured intents JSON. We provide:
1. **Skill document** (`llm_skill/layout_intent_translator.md`) — system prompt for the LLM. Contains intent schema, device name resolution rules, translation rules, and PCR workflow examples.
2. **Runtime schema** (`GET /interpret/schema`) — machine-readable intent specs. LLM agent should call this for discovery.
3. **Device context** — before translating, feed the LLM the scene's device list (from `GET /devices` or your scene state). The LLM uses this to resolve fuzzy names like "PCR machine" → `inheco_odtc_96xl`.
### Integration Flow
```
1. User enters NL request in Cloud UI
2. Your LLM agent receives:
- User message
- Scene device list (id, name, type, bbox)
- Skill doc as system prompt
- Optional: GET /interpret/schema for discovery
3. LLM outputs: {"intents": [...]}
4. POST /interpret with LLM output
5. Show user the translations for confirmation
6. POST /optimize with confirmed constraints + workflow_edges
7. Apply placements to scene
```
### Device Name Resolution (handled by LLM, not by optimizer)
The skill doc teaches the LLM to match fuzzy names:
- "PCR machine" / "thermal cycler" → `inheco_odtc_96xl`
- "liquid handler" / "pipetting robot" → `opentrons_liquid_handler`
- "plate hotel" / "storage" → `thermo_orbitor_rs2_hotel`
- "robot arm" / "the arm" → device with `type: articulation`
- "plate sealer" → `agilent_plateloc`
No search endpoint needed — the device list is already in context.
### Tested LLM Outputs
We tested with Claude Sonnet (via subagent, no API key required). Examples:
**Input**: "Take plate from hotel, prepare sample in the pipetting robot, seal it, then run thermal cycling. The arm handles all transfers. Keep liquid handler and sealer close, minimum 15cm gap."
**LLM produced**: `reachable_by` (arm→4 devices), `workflow_hint` (correct PCR order), `close_together` (high, LH+sealer), `min_distance` (0.15m, LH+sealer)
**Input**: "I want an automatic PCR lab, make it compact and neat"
**LLM produced**: `reachable_by`, `workflow_hint`, `close_together` (all devices), `min_spacing` (0.05m), `align_cardinal`
All outputs pass through `/interpret``/optimize` successfully.
---
## 5. Constraint System Details
### Hard Constraints (cost = ∞ on violation)
| Rule Name | Params | What it checks |
|-----------|--------|---------------|
| `no_collision` | (default, always on) | OBB-SAT pairwise collision between all devices |
| `within_bounds` | (default, always on) | All devices within lab boundary |
| `reachability` | `arm_id`, `target_device_id` | Target center within arm reach radius |
| `distance_less_than` | `device_a`, `device_b`, `distance` | OBB edge-to-edge distance ≤ threshold |
| `distance_greater_than` | `device_a`, `device_b`, `distance` | OBB edge-to-edge distance ≥ threshold |
| `min_spacing` | `min_gap` | All device pairs have ≥ min_gap edge-to-edge |
### Soft Constraints (weighted penalty)
| Rule Name | Params | What it minimizes |
|-----------|--------|------------------|
| `minimize_distance` | `device_a`, `device_b` | OBB edge-to-edge distance × weight |
| `maximize_distance` | `device_a`, `device_b` | 1/(distance+ε) × weight |
| `prefer_orientation_mode` | `mode` (outward/inward) | Angle between opening direction and ideal direction |
| `prefer_aligned` | (none) | Deviation from nearest 90° angle |
| `prefer_seeder_orientation` | (none) | Deviation from seeder-assigned θ |
| `crossing_penalty` | (auto, part of `reachability` eval) | Segment-OBB intersection length of opening-to-arm path blocked by other devices (Cyrus-Beck clipping via `obb.segment_obb_intersection_length`) |
### Weight Normalization
| Constant | Value | Meaning |
|----------|-------|---------|
| `DEFAULT_WEIGHT_DISTANCE` | 100.0 | 1 cm → penalty 1.0 |
| `DEFAULT_WEIGHT_ANGLE` | 60.0 | 5° → penalty ~1.0 |
| `HARD_MULTIPLIER` | 5.0 | Hard constraint penalty multiplier during graduated DE |
Constraints support a `priority` field (`critical` / `high` / `normal` / `low`) with multipliers 5× / 2× / 1× / 0.5×.
### Graduated Penalties (DE internals)
Default hard constraints (collision, boundary) use **graduated penalties** during DE optimization — proportional to penetration depth / overshoot distance. This gives DE a smooth gradient instead of binary inf. Final evaluation uses binary mode for pass/fail reporting.
---
## 6. Checker Architecture (Mock → Real)
```
interfaces.py (Protocol definitions)
├── CollisionChecker.check(placements) → collisions
├── CollisionChecker.check_bounds(placements, w, d) → out_of_bounds
└── ReachabilityChecker.is_reachable(arm_id, arm_pose, target) → bool
mock_checkers.py (current, no ROS)
├── MockCollisionChecker — OBB SAT
└── MockReachabilityChecker — Euclidean distance, 100m fallback for unknown arms
ros_checkers.py (for ROS2/MoveIt2 integration)
├── MoveItCollisionChecker — python-fcl direct + OBB fallback
└── IKFastReachabilityChecker — precomputed voxel O(1) + live IK fallback
└── create_checkers(mode) — factory, controlled by LAYOUT_CHECKER_MODE env var
```
To switch to real checkers: `LAYOUT_CHECKER_MODE=moveit` + pass MoveIt2 instance.
---
## 7. File Inventory
### Core Pipeline
| File | Lines | Purpose |
|------|-------|---------|
| `models.py` | 97 | Dataclasses: Device, Lab, Placement, Constraint, Intent, Opening |
| `device_catalog.py` | 303 | Loads devices from footprints.json + uni-lab-assets + registry |
| `footprints.json` | 183KB | 499 device bounding boxes, heights, openings (offline extracted) |
| `seeders.py` | 331 | Force-directed initial layout with presets |
| `optimizer.py` | 1056 | Custom DE loop: per-device crossover, θ wrapping, discrete angle lattice, multi-strategy |
| `broad_phase.py` | 66 | 2-axis sweep-and-prune AABB broad phase for collision pair pruning |
| `constraints.py` | 627 | Unified constraint evaluation (hard + soft + graduated + crossing penalty) |
| `obb.py` | 257 | OBB geometry: corners, overlap SAT, min_distance, penetration_depth, segment intersection |
| `intent_interpreter.py` | 366 | 11 intent handlers, pure translation, no side effects |
| `server.py` | 743 | FastAPI: /interpret, /optimize, /devices, /scene/* endpoints |
| `lab_parser.py` | 50 | Parse lab floor plan JSON to Lab dataclass |
### Reference / Utilities
| File | Purpose |
|------|---------|
| `extract_footprints.py` | How footprints.json was generated (offline STL/GLB → 2D bbox extraction via trimesh) |
| `generate_asset_registry.py` | Generate YAML registry entries for uni-lab-assets devices not already registered |
### Integration Layer
| File | Purpose |
|------|---------|
| `interfaces.py` | Protocol definitions for CollisionChecker / ReachabilityChecker |
| `mock_checkers.py` | Dev-mode checkers (OBB collision, Euclidean reachability) |
| `ros_checkers.py` | MoveIt2/IKFast adapters for real collision + reachability |
### LLM
| File | Purpose |
|------|---------|
| `llm_skill/layout_intent_translator.md` | System prompt for LLM: intent schema, device resolution, translation rules, examples |
| `llm_skill/demo_agent.md` | LLM agent orchestration instructions for demo (GET /devices → intents → /interpret → /optimize → /scene/placements) |
### Demo / Frontend
| File | Purpose |
|------|---------|
| `static/lab3d.html` | Three.js 3D visualization frontend (1227 lines): device library, drag-to-add, auto layout, scene polling |
### Configuration
| File | Purpose |
|------|---------|
| `pyproject.toml` | Package deps: scipy, numpy, fastapi, uvicorn, pydantic |
### Tests (270 total: 260 pass + 10 skip without API key)
| File | Tests | Coverage |
|------|-------|----------|
| `test_intent_interpreter.py` | 19 | All 11 handlers, validation, priority, multi-intent |
| `test_interpret_api.py` | 6 | /interpret and /interpret/schema endpoints |
| `test_e2e_pcr_pipeline.py` | 12 | Full pipeline: interpret → optimize → verify placements |
| `test_llm_skill.py` | 10 | Real LLM fuzzy input → structured output (needs ANTHROPIC_API_KEY) |
| `test_constraints.py` | 30 | Constraint evaluation, hard/soft, graduated penalties, crossing penalty |
| `test_optimizer.py` | 50 | DE optimizer, vector encoding, bounds, discrete angles, strategies |
| `test_mock_checkers.py` | 15 | MockCollisionChecker, MockReachabilityChecker |
| `test_ros_checkers.py` | 40 | MoveIt2/IKFast adapter tests |
| `test_seeders.py` | 12 | Force-directed seeder presets |
| `test_device_catalog.py` | 25 | Device loading, footprint merging |
| `test_obb.py` | 18 | OBB geometry functions, segment intersection |
| `test_bugfixes_v2.py` | 28 | Regression: duplicate IDs, orientation, min_spacing, cardinal snap defaults |
| `test_broad_phase.py` | 5 | Sweep-and-prune AABB broad phase |
---
## 8. How to Run
### Quick Start
```bash
# Install
pip install -e ".[dev]"
# Run server
uvicorn unilabos.layout_optimizer.server:app --host 0.0.0.0 --port 8000 --reload
# Run server with debug logging (shows DE cost breakdown per generation)
LAYOUT_DEBUG=1 uvicorn unilabos.layout_optimizer.server:app --host 0.0.0.0 --port 8000 --reload
# Run tests
pytest unilabos/layout_optimizer/tests/ -v
# Run LLM skill tests (needs API key)
ANTHROPIC_API_KEY=sk-... pytest unilabos/layout_optimizer/tests/test_llm_skill.py -v
```
**Log files**: All requests are logged to `unilabos/layout_optimizer/logs/{YYYYMMDD_HHMMSS}.log` at DEBUG level (frontend polling GET /scene/placements excluded).
### Dependencies
- Python ≥ 3.10
- scipy, numpy, fastapi, uvicorn, pydantic
- Optional: anthropic (for LLM skill tests)
- Optional: python-fcl (for real collision checking, not needed for mock mode)
### Environment Variables
| Variable | Default | Purpose |
|----------|---------|---------|
| `UNI_LAB_ASSETS_DIR` | `../uni-lab-assets` | Path to device 3D models |
| `UNI_LAB_OS_DEVICE_MESH_DIR` | `Uni-Lab-OS/unilabos/device_mesh/devices` | Registry device meshes |
| `LAYOUT_CHECKER_MODE` | `mock` | `mock` or `moveit` for checker selection |
| `LAYOUT_DEBUG` | (unset) | Set to `1` for DEBUG-level console logging (DE cost breakdown per generation) |
| `ANTHROPIC_API_KEY` | (none) | For LLM skill tests |
---
## 9. Known Limitations
1. **Mock reachability**: `MockReachabilityChecker` uses 100m fallback for unknown arm IDs — effectively "always reachable" for mock mode. Real arm reach requires `ros_checkers.py` with MoveIt2.
2. **No real LLM in tests**: `test_llm_skill.py` tests are skipped without `ANTHROPIC_API_KEY`. We verified with Claude Sonnet subagent that the skill doc produces correct output for PCR workflow scenarios.
3. **Opening data coverage**: 289/499 devices have opening direction annotations. Devices without openings default to local -Y as front with no alignment penalty.
4. **Single lab room**: No multi-room or corridor support yet. Lab is a single rectangle with optional rectangular obstacles.
5. **Intent interpreter is stateless**: It translates intents one-by-one with no cross-referencing between them. Duplicate/conflicting constraints are the LLM's responsibility to avoid.
6. **`align_weight` and `snap_cardinal` default to off**: `prefer_aligned` weight defaults to 0 (was `DEFAULT_WEIGHT_ANGLE=60`) and `snap_theta_safe` is opt-in via `snap_cardinal=True`. Both remain available when explicitly requested via `align_cardinal` intent or API param.
7. **Hybrid angle mode deprecated**: The angle-first hybrid mode (separate angle sweep + position-only DE) has been replaced by joint discrete mode as the default when `angle_granularity` is set. Joint mode snaps θ to the discrete lattice within the normal 3N DE loop.
---
## 10. Quick Verification (curl)
```bash
# 1. Health check
curl http://localhost:8000/health
# 2. Schema discovery
curl http://localhost:8000/interpret/schema | python3 -m json.tool
# 3. Interpret PCR workflow
curl -X POST http://localhost:8000/interpret \
-H "Content-Type: application/json" \
-d '{
"intents": [
{"intent": "reachable_by", "params": {"arm": "arm_slider", "targets": ["opentrons_liquid_handler", "inheco_odtc_96xl"]}, "description": "arm reaches targets"},
{"intent": "workflow_hint", "params": {"workflow": "pcr", "devices": ["thermo_orbitor_rs2_hotel", "opentrons_liquid_handler", "agilent_plateloc", "inheco_odtc_96xl"]}, "description": "PCR order"}
]
}' | python3 -m json.tool
# 4. Optimize (use constraints from step 3)
curl -X POST http://localhost:8000/optimize \
-H "Content-Type: application/json" \
-d '{
"devices": [
{"id": "thermo_orbitor_rs2_hotel", "name": "Plate Hotel"},
{"id": "arm_slider", "name": "Robot Arm", "device_type": "articulation"},
{"id": "opentrons_liquid_handler", "name": "Liquid Handler"},
{"id": "agilent_plateloc", "name": "Plate Sealer"},
{"id": "inheco_odtc_96xl", "name": "Thermal Cycler"}
],
"lab": {"width": 6.0, "depth": 4.0},
"constraints": [
{"type": "hard", "rule_name": "reachability", "params": {"arm_id": "arm_slider", "target_device_id": "opentrons_liquid_handler"}, "weight": 1.0},
{"type": "hard", "rule_name": "reachability", "params": {"arm_id": "arm_slider", "target_device_id": "inheco_odtc_96xl"}, "weight": 1.0},
{"type": "soft", "rule_name": "minimize_distance", "params": {"device_a": "thermo_orbitor_rs2_hotel", "device_b": "opentrons_liquid_handler"}, "weight": 3.0},
{"type": "soft", "rule_name": "minimize_distance", "params": {"device_a": "opentrons_liquid_handler", "device_b": "agilent_plateloc"}, "weight": 3.0},
{"type": "soft", "rule_name": "minimize_distance", "params": {"device_a": "agilent_plateloc", "device_b": "inheco_odtc_96xl"}, "weight": 3.0}
],
"workflow_edges": [
["thermo_orbitor_rs2_hotel", "opentrons_liquid_handler"],
["opentrons_liquid_handler", "agilent_plateloc"],
["agilent_plateloc", "inheco_odtc_96xl"]
],
"run_de": true,
"angle_granularity": 4,
"maxiter": 100,
"seed": 42
}' | python3 -m json.tool
```
---
## 11. Demo Setup
This section documents the device processing pipeline, test frontend, and LLM agent demo for the layout optimizer.
### 11.1 Device Processing Pipeline
How devices go from 3D meshes to collision footprints:
1. **Source data**:
- `uni-lab-assets/` repository: GLB/STL 3D models + XACRO robot descriptions
- `Uni-Lab-OS/device_mesh/devices/` registry: device metadata directories
2. **Extraction** (`extract_footprints.py`):
- Load meshes via `trimesh` (STL for geometry, GLB for display)
- Compute oriented bounding box (OBB): width, depth, height
- Apply GLB root node rotation to align with world frame
- Detect openings from XACRO `<joint type="fixed">` elements containing "socket" in name
- Compute opening direction: centroid of socket origins → cardinal direction mapping
- Manual overrides for devices with non-standard opening patterns (`MANUAL_OPENINGS` dict)
- Write results to `footprints.json` (499 devices, 183KB)
3. **Catalog merging** (`device_catalog.py`):
- Load `footprints.json` (OBB + openings)
- Load `uni-lab-assets/data.json` (asset tree structure)
- Load `Uni-Lab-OS/device_mesh/devices/` (registry devices)
- Merge: registry devices get priority for metadata, but assets' 3D model paths preferred
- Fallback sizes: `KNOWN_SIZES` dict provides manual dimensions when trimesh extraction fails
4. **Standalone filtering** (`server.py:_is_standalone_device`):
- Bbox >30cm = device (standalone equipment)
- Bbox <5cm = consumable (plates, tubes, tips)
- 5-30cm = keyword heuristic (check name for "plate", "tube", "tip", "rack")
### 11.2 Test Frontend (`static/lab3d.html`)
Interactive 3D lab layout visualization and design tool (1227 lines).
**Technology stack**:
- Three.js v0.169.0 (ES modules from esm.sh CDN)
- WebGL renderer with PCF soft shadow maps, ACES filmic tone mapping
- OrbitControls for camera interaction
**Features**:
- **Device library**: Left sidebar with search/filter, toggle between devices and consumables
- **Drag-to-add**: Click device in library → adds to scene with random position
- **Selected devices panel**: Right panel lists all placed devices, click to remove
- **Lab dimensions**: Width × Depth inputs (meters), collision margin slider
- **View modes**: 3D perspective (default) and top-down orthographic
- **Grid system**: 0.5m grid with lab boundary highlighting
- **Device visualization**: Box geometry with emissive materials, edge highlights, CSS2D labels
- **Opening markers**: Orange arrows and semi-transparent strips showing device access directions
- **Auto Layout button**: Calls `POST /optimize` with current devices + constraints
- **Scene polling**: 1-second polling of `GET /scene/placements` for agent-pushed updates (version-based change detection)
- **Smooth animation**: Lerp interpolation for device placement changes
**Backend integration**:
- `GET /devices` — Load device catalog on startup
- `POST /optimize` — Send devices + constraints, receive placements
- `POST /scene/lab` — Push lab dimensions when changed
- `GET /scene/placements` — Poll every 1s for agent-pushed updates
**Key JavaScript functions**:
- `loadDeviceCatalog()` — Fetch device list, build catalog with color pool
- `createDeviceMesh(deviceId, uuid)` — Create Three.js Group with body, edges, opening markers
- `addDevice(deviceId)` / `removeDevice(uuid)` — Manage selected devices
- `runLayout()` — Call backend `/optimize` or local bin packing fallback
- `animatePlacement(uuid, tx, tz, theta)` — Smooth lerp to target position
- `setView('3d' | 'top')` — Switch camera perspective
### 11.3 LLM Agent Demo (`llm_skill/demo_agent.md`)
LLM agent orchestration instructions for natural language lab layout design.
**Agent workflow**:
1. `GET /devices` — Fetch device catalog for context
2. Parse user natural language request
3. Build structured intents JSON (using `layout_intent_translator.md` skill)
4. `POST /interpret` — Translate intents to constraints
5. `POST /optimize` — Run layout optimization
6. `POST /scene/placements` — Push results to shared scene state
7. Frontend auto-updates via polling (no manual refresh needed)
**Example user requests**:
- "Design a PCR lab with robot arm automation, keep it compact"
- "Place liquid handler, thermal cycler, and plate sealer. Arm must reach all devices."
- "Add a plate hotel, make sure it's close to the liquid handler"
### 11.4 Running the Demo
```bash
# Start the server
uvicorn unilabos.layout_optimizer.server:app --host 0.0.0.0 --port 8000 --reload
# Open in browser
# http://localhost:8000/
# Use Claude Code with demo_agent.md skill to orchestrate via natural language
# The agent will call the API endpoints and push results to /scene/placements
# The frontend will automatically update via polling
```
**Demo flow**:
1. Open `http://localhost:8000/` in browser
2. Frontend loads device catalog and displays 3D scene
3. Use Claude Code with `demo_agent.md` skill to send natural language requests
4. Agent translates request → intents → constraints → optimization → scene update
5. Frontend polls `/scene/placements` every 1s and animates changes
6. User can manually add/remove devices or adjust lab size in the UI
7. Click "Auto Layout" to re-optimize with current devices

View File

@@ -1,9 +0,0 @@
"""Layout Optimizer — AI 实验室布局自动排布。
独立开发包,无 ROS 依赖。集成阶段合并到 Uni-Lab-OS。
"""
from .models import Constraint, Device, Lab, Opening, Placement
from .optimizer import optimize
__all__ = ["Device", "Lab", "Opening", "Placement", "Constraint", "optimize"]

View File

@@ -1,66 +0,0 @@
"""2 轴 sweep-and-prune 宽相碰撞检测。
对每个设备计算旋转后的 AABB先沿 x 轴排序并剪枝,
再用 y 轴交叠过滤。返回候选碰撞对(索引对列表),
供后续 OBB SAT 精确检测使用。
"""
from __future__ import annotations
from .models import Device, Placement
def sweep_and_prune_pairs(
devices: list[Device],
placements: list[Placement],
) -> list[tuple[int, int]]:
"""2 轴 sweep-and-prune返回 AABB 交叠的索引对。
Args:
devices: 设备列表,与 placements 一一对应。
placements: 布局位姿列表。
Returns:
候选碰撞对列表,每个元素为 (i, j)
i < j索引对应 placements 原始顺序。
"""
n = len(devices)
if n < 2:
return []
# --- 计算每个设备旋转后的 AABB ---
aabbs: list[tuple[float, float, float, float]] = []
for dev, pl in zip(devices, placements):
hw, hd = pl.rotated_bbox(dev)
aabbs.append((pl.x - hw, pl.x + hw, pl.y - hd, pl.y + hd))
# --- 按 xmin 排序,保留原始索引映射 ---
sorted_indices = sorted(range(n), key=lambda k: aabbs[k][0])
# --- 扫描 x 轴y 轴过滤 ---
candidates: list[tuple[int, int]] = []
for si in range(len(sorted_indices)):
i = sorted_indices[si]
x_min_i, x_max_i, y_min_i, y_max_i = aabbs[i]
for sj in range(si + 1, len(sorted_indices)):
j = sorted_indices[sj]
x_min_j, _x_max_j, y_min_j, y_max_j = aabbs[j]
# 由于按 xmin 排序x_min_j >= x_min_i
if x_min_j > x_max_i:
break # 后续设备 xmin 更大,不可能与 i 在 x 轴交叠
# x 轴交叠确认,检查 y 轴
if y_min_i <= y_max_j and y_min_j <= y_max_i:
# 保证输出 (min_idx, max_idx) 方便去重和测试
pair = (min(i, j), max(i, j))
candidates.append(pair)
return candidates
def broad_phase_device_pairs(
devices: list[Device],
placements: list[Placement],
) -> list[tuple[str, str]]:
"""返回候选碰撞对的 device_id 字符串元组列表。"""
index_pairs = sweep_and_prune_pairs(devices, placements)
return [(placements[i].device_id, placements[j].device_id) for i, j in index_pairs]

View File

@@ -1,626 +0,0 @@
"""约束体系:硬约束 / 软约束定义与统一评估。
硬约束违反 → cost = inf方案直接淘汰
软约束违反 → 加权 penalty 累加到 cost
"""
from __future__ import annotations
import logging
import math
from typing import TYPE_CHECKING
from .broad_phase import sweep_and_prune_pairs
from .models import Constraint, Device, Lab, Placement
from .obb import (
nearest_point_on_obb,
obb_corners,
obb_min_distance,
obb_penetration_depth,
segment_obb_intersection_length,
)
if TYPE_CHECKING:
from typing import Any
from .interfaces import CollisionChecker, ReachabilityChecker
logger = logging.getLogger(__name__)
# 归一化默认权重 — 1cm距离违规 ≈ 5°角度违规 的惩罚量级
DEFAULT_WEIGHT_DISTANCE: float = 100.0 # 1cm → penalty 1.0
DEFAULT_WEIGHT_ANGLE: float = 60.0 # 5° → penalty ~1.0
# 硬约束graduated模式下的惩罚倍数
HARD_MULTIPLIER: float = 5.0
# 优先级等级对应的权重乘数
PRIORITY_MULTIPLIERS: dict[str, float] = {
"critical": 5.0,
"high": 2.0,
"normal": 1.0,
"low": 0.5,
}
def evaluate_constraints(
devices: list[Device],
placements: list[Placement],
lab: Lab,
constraints: list[Constraint],
collision_checker: CollisionChecker,
reachability_checker: ReachabilityChecker | None = None,
*,
graduated: bool = True,
) -> float:
"""统一评估所有约束,返回总 cost。
Args:
devices: 设备列表(与 placements 一一对应)
placements: 当前布局方案
lab: 实验室平面图
constraints: 约束规则列表
collision_checker: 碰撞检测实例
reachability_checker: 可达性检测实例(可选)
graduated: True=比例惩罚DE优化用False=二值inf最终pass/fail用
Returns:
总 cost。硬约束违反在非graduated模式返回 inf否则为加权 penalty 之和。
"""
device_map = {d.id: d for d in devices}
placement_map = {p.device_id: p for p in placements}
total_cost = 0.0
for c in constraints:
cost = _evaluate_single(
c, device_map, placement_map, lab, collision_checker, reachability_checker,
graduated=graduated,
)
if math.isinf(cost):
return math.inf
total_cost += cost
return total_cost
def evaluate_default_hard_constraints(
devices: list[Device],
placements: list[Placement],
lab: Lab,
collision_checker: CollisionChecker,
*,
graduated: bool = True,
collision_weight: float = DEFAULT_WEIGHT_DISTANCE * HARD_MULTIPLIER, # 500
boundary_weight: float = DEFAULT_WEIGHT_DISTANCE * HARD_MULTIPLIER, # 500
) -> float:
"""评估默认硬约束(碰撞 + 边界),无需显式声明约束列表。
始终生效,用于 cost function 的基础检查。
When graduated=True (default), returns a penalty proportional to the
severity of each violation instead of binary inf. This gives DE a
smooth gradient so it can fix specific collision pairs instead of
discarding near-optimal layouts entirely.
When graduated=False, uses the legacy binary inf behaviour.
"""
if not graduated:
return _evaluate_hard_binary(devices, placements, lab, collision_checker)
device_map = {d.id: d for d in devices}
cost = 0.0
# Graduated collision penalty: 2 轴 sweep-and-prune 宽相 + OBB SAT 精确检测
candidate_pairs = sweep_and_prune_pairs(devices, placements)
for i, j in candidate_pairs:
di, dj = device_map[placements[i].device_id], device_map[placements[j].device_id]
ci = obb_corners(placements[i].x, placements[i].y,
di.bbox[0], di.bbox[1], placements[i].theta)
cj = obb_corners(placements[j].x, placements[j].y,
dj.bbox[0], dj.bbox[1], placements[j].theta)
depth = obb_penetration_depth(ci, cj)
if depth > 0:
cost += collision_weight * depth
# Graduated boundary penalty: sum of overshoot distances (rotation-aware)
for p in placements:
dev = device_map[p.device_id]
hw, hd = p.rotated_bbox(dev)
# How far each edge exceeds the lab boundary
overshoot = 0.0
overshoot += max(0.0, hw - p.x) # left wall
overshoot += max(0.0, (p.x + hw) - lab.width) # right wall
overshoot += max(0.0, hd - p.y) # bottom wall
overshoot += max(0.0, (p.y + hd) - lab.depth) # top wall
cost += boundary_weight * overshoot
return cost
def _evaluate_hard_binary(
devices: list[Device],
placements: list[Placement],
lab: Lab,
collision_checker: CollisionChecker,
) -> float:
"""Legacy binary hard-constraint evaluation (inf or 0)."""
checker_placements = _to_checker_format(devices, placements)
collisions = collision_checker.check(checker_placements)
if collisions:
return math.inf
if hasattr(collision_checker, "check_bounds"):
oob = collision_checker.check_bounds(checker_placements, lab.width, lab.depth)
if oob:
return math.inf
return 0.0
def _evaluate_single(
constraint: Constraint,
device_map: dict[str, Device],
placement_map: dict[str, Placement],
lab: Lab,
collision_checker: CollisionChecker,
reachability_checker: ReachabilityChecker | None,
*,
graduated: bool = True,
) -> float:
"""评估单条约束规则。
graduated=True 时硬约束返回比例惩罚DE用
graduated=False 时硬约束返回 inf最终 pass/fail
"""
rule = constraint.rule_name
params = constraint.params
is_hard = constraint.type == "hard"
effective_weight = constraint.weight
if rule == "no_collision":
checker_placements = _to_checker_format_from_maps(device_map, placement_map)
collisions = collision_checker.check(checker_placements)
if collisions:
if is_hard and not graduated:
return math.inf
w = effective_weight * (HARD_MULTIPLIER if is_hard else 1.0)
return w * len(collisions)
return 0.0
if rule == "within_bounds":
checker_placements = _to_checker_format_from_maps(device_map, placement_map)
if hasattr(collision_checker, "check_bounds"):
oob = collision_checker.check_bounds(
checker_placements, lab.width, lab.depth
)
if oob:
if is_hard and not graduated:
return math.inf
w = effective_weight * (HARD_MULTIPLIER if is_hard else 1.0)
return w * len(oob)
return 0.0
if rule == "distance_less_than":
a_id, b_id = params["device_a"], params["device_b"]
max_dist = params["distance"]
da, db = device_map.get(a_id), device_map.get(b_id)
pa, pb = placement_map.get(a_id), placement_map.get(b_id)
missing_cost = _missing_reference_cost(constraint, placement_map, a_id, b_id)
if missing_cost is not None:
return missing_cost
if da and db:
dist = _device_distance_obb(da, pa, db, pb)
else:
dist = _device_distance_center(pa, pb) or 0.0
if dist > max_dist:
if is_hard and not graduated:
return math.inf
w = effective_weight * (HARD_MULTIPLIER if is_hard else 1.0)
return w * (dist - max_dist)
return 0.0
if rule == "distance_greater_than":
a_id, b_id = params["device_a"], params["device_b"]
min_dist = params["distance"]
da, db = device_map.get(a_id), device_map.get(b_id)
pa, pb = placement_map.get(a_id), placement_map.get(b_id)
missing_cost = _missing_reference_cost(constraint, placement_map, a_id, b_id)
if missing_cost is not None:
return missing_cost
if da and db:
dist = _device_distance_obb(da, pa, db, pb)
else:
dist = _device_distance_center(pa, pb) or 0.0
if dist < min_dist:
if is_hard and not graduated:
return math.inf
w = effective_weight * (HARD_MULTIPLIER if is_hard else 1.0)
return w * (min_dist - dist)
return 0.0
if rule == "minimize_distance":
a_id, b_id = params["device_a"], params["device_b"]
da, db = device_map.get(a_id), device_map.get(b_id)
pa, pb = placement_map.get(a_id), placement_map.get(b_id)
missing_cost = _missing_reference_cost(constraint, placement_map, a_id, b_id)
if missing_cost is not None:
return missing_cost
if da and db:
dist = _device_distance_obb(da, pa, db, pb)
else:
dist = _device_distance_center(pa, pb) or 0.0
return effective_weight * dist
if rule == "maximize_distance":
a_id, b_id = params["device_a"], params["device_b"]
da, db = device_map.get(a_id), device_map.get(b_id)
pa, pb = placement_map.get(a_id), placement_map.get(b_id)
missing_cost = _missing_reference_cost(constraint, placement_map, a_id, b_id)
if missing_cost is not None:
return missing_cost
if da and db:
dist = _device_distance_obb(da, pa, db, pb)
else:
dist = _device_distance_center(pa, pb) or 0.0
max_possible = math.sqrt(lab.width**2 + lab.depth**2)
return effective_weight * (max_possible - dist)
if rule == "min_spacing":
min_gap = params.get("min_gap", 0.0)
all_placements = list(placement_map.values())
total_penalty = 0.0
for i in range(len(all_placements)):
for j in range(i + 1, len(all_placements)):
pi, pj = all_placements[i], all_placements[j]
di = device_map.get(pi.device_id)
dj = device_map.get(pj.device_id)
if di and dj:
dist = _device_distance_obb(di, pi, dj, pj)
else:
dist = _device_distance_center(pi, pj) or 0.0
if dist < min_gap:
total_penalty += (min_gap - dist)
if total_penalty > 0:
if is_hard and not graduated:
return math.inf
w = effective_weight * (HARD_MULTIPLIER if is_hard else 1.0)
return w * total_penalty
return 0.0
if rule == "reachability":
if reachability_checker is None:
return 0.0
arm_id = params["arm_id"]
target_device_id = params["target_device_id"]
arm_p = placement_map.get(arm_id)
target_p = placement_map.get(target_device_id)
missing_cost = _missing_reference_cost(
constraint, placement_map, arm_id, target_device_id,
)
if missing_cost is not None:
return missing_cost
arm_dev = device_map.get(arm_id)
target_dev = device_map.get(target_device_id)
# opening surface center → nearest point on arm OBB
if arm_dev and target_dev:
opening_pt = _opening_surface_center(target_dev, target_p)
arm_corners = obb_corners(
arm_p.x, arm_p.y, arm_dev.bbox[0], arm_dev.bbox[1], arm_p.theta,
)
nearest = nearest_point_on_obb(opening_pt[0], opening_pt[1], arm_corners)
dist = math.sqrt((opening_pt[0] - nearest[0])**2 + (opening_pt[1] - nearest[1])**2)
else:
opening_pt = (target_p.x, target_p.y)
nearest = (arm_p.x, arm_p.y)
dist = _device_distance_center(arm_p, target_p) or 0.0
# 交叉惩罚始终计算soft, 不依赖可达性结果)
crossing_cost = _crossing_penalty(
opening_pt, nearest,
arm_id, target_device_id,
device_map, placement_map,
)
arm_pose = {"x": arm_p.x, "y": arm_p.y, "theta": arm_p.theta}
target_point = {"x": target_p.x, "y": target_p.y, "z": 0.0}
target_point["_obb_dist"] = dist
if not reachability_checker.is_reachable(arm_id, arm_pose, target_point):
if is_hard and not graduated:
return math.inf
# Graduated: overshoot penalty + crossing cost
max_reach = reachability_checker.arm_reach.get(arm_id, 2.0)
overshoot = max(0.0, dist - max_reach)
w = effective_weight * (HARD_MULTIPLIER if is_hard else 1.0)
return w * overshoot * 10.0 + crossing_cost
return crossing_cost
if rule == "prefer_aligned":
alignment_cost = sum(
(1 - math.cos(4 * p.theta)) / 2 for p in placement_map.values()
)
if is_hard:
if not graduated:
return math.inf if alignment_cost > 1e-6 else 0.0
return HARD_MULTIPLIER * effective_weight * alignment_cost
return effective_weight * alignment_cost
if rule == "prefer_seeder_orientation":
target_thetas = params.get("target_thetas", {})
cost = 0.0
for dev_id, target in target_thetas.items():
p = placement_map.get(dev_id)
if p is None:
continue
# Circular distance: (1 - cos(diff)) / 2 gives 0..1 range
diff = p.theta - target
cost += (1 - math.cos(diff)) / 2
return effective_weight * cost
if rule == "prefer_orientation_mode":
mode = params.get("mode", "outward")
center_x = lab.width / 2
center_y = lab.depth / 2
cost = 0.0
for dev_id, p in placement_map.items():
dev = device_map.get(dev_id)
if dev is None:
continue
target = _desired_theta(
p.x, p.y, center_x, center_y, dev, mode,
)
if target is None:
continue
diff = p.theta - target
cost += (1 - math.cos(diff)) / 2
return effective_weight * cost
# 未知约束类型,忽略
return 0.0
def _desired_theta(
x: float, y: float,
center_x: float, center_y: float,
device: Device, mode: str,
) -> float | None:
"""Compute desired theta for outward/inward facing at the given position."""
dx = x - center_x
dy = y - center_y
if abs(dx) < 1e-9 and abs(dy) < 1e-9:
return None # At center, no preferred direction
angle_to_device = math.atan2(dy, dx)
front = device.openings[0].direction if device.openings else (0.0, -1.0)
front_angle = math.atan2(front[1], front[0])
if mode == "outward":
target = angle_to_device
elif mode == "inward":
target = angle_to_device + math.pi
else:
return None
return (target - front_angle) % (2 * math.pi)
def _device_distance_center(a: Placement | None, b: Placement | None) -> float | None:
"""计算两设备中心的欧几里得距离(后备方法)。"""
if a is None or b is None:
return None
return math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2)
def _device_distance_obb(
device_a: Device, placement_a: Placement,
device_b: Device, placement_b: Placement,
) -> float:
"""Minimum edge-to-edge distance between two devices using OBB."""
corners_a = obb_corners(
placement_a.x, placement_a.y,
device_a.bbox[0], device_a.bbox[1],
placement_a.theta,
)
corners_b = obb_corners(
placement_b.x, placement_b.y,
device_b.bbox[0], device_b.bbox[1],
placement_b.theta,
)
return obb_min_distance(corners_a, corners_b)
def _to_checker_format(
devices: list[Device], placements: list[Placement]
) -> list[dict]:
"""转换为 CollisionChecker.check() 接受的格式。"""
device_map = {d.id: d for d in devices}
result = []
for p in placements:
dev = device_map.get(p.device_id)
if dev is None:
continue
result.append({"id": p.device_id, "bbox": dev.bbox, "pos": (p.x, p.y, p.theta)})
return result
def _to_checker_format_from_maps(
device_map: dict[str, Device], placement_map: dict[str, Placement]
) -> list[dict]:
"""从 map 转换为 CollisionChecker.check() 接受的格式。"""
result = []
for dev_id, p in placement_map.items():
dev = device_map.get(dev_id)
if dev is None:
continue
result.append({"id": dev_id, "bbox": dev.bbox, "pos": (p.x, p.y, p.theta)})
return result
def _opening_surface_center(
device: Device, placement: Placement,
) -> tuple[float, float]:
"""Return the world-space center of the device's opening surface.
Computes where the opening direction intersects the device's bbox boundary,
then transforms to world coordinates. For a device facing away from the arm,
this point is on the far side — making the distance to the arm larger,
which naturally penalizes wrong orientation.
"""
front = device.openings[0].direction if device.openings else (0.0, -1.0)
dx, dy = front
w, h = device.bbox
# Scale factor to reach bbox edge in the opening direction
scales = []
if abs(dx) > 1e-9:
scales.append((w / 2) / abs(dx))
if abs(dy) > 1e-9:
scales.append((h / 2) / abs(dy))
scale = min(scales) if scales else 0.0
# Opening center in local frame
local_x = dx * scale
local_y = dy * scale
# Rotate to world frame and translate
cos_t = math.cos(placement.theta)
sin_t = math.sin(placement.theta)
world_x = placement.x + local_x * cos_t - local_y * sin_t
world_y = placement.y + local_x * sin_t + local_y * cos_t
return (world_x, world_y)
def evaluate_default_hard_constraints_breakdown(
devices: list[Device],
placements: list[Placement],
lab: Lab,
collision_checker: CollisionChecker,
*,
collision_weight: float = DEFAULT_WEIGHT_DISTANCE * HARD_MULTIPLIER,
boundary_weight: float = DEFAULT_WEIGHT_DISTANCE * HARD_MULTIPLIER,
) -> dict[str, float]:
"""与 evaluate_default_hard_constraints 逻辑相同,但返回分项明细。"""
device_map = {d.id: d for d in devices}
collision_cost = 0.0
boundary_cost = 0.0
candidate_pairs = sweep_and_prune_pairs(devices, placements)
for i, j in candidate_pairs:
di, dj = device_map[placements[i].device_id], device_map[placements[j].device_id]
ci = obb_corners(placements[i].x, placements[i].y,
di.bbox[0], di.bbox[1], placements[i].theta)
cj = obb_corners(placements[j].x, placements[j].y,
dj.bbox[0], dj.bbox[1], placements[j].theta)
depth = obb_penetration_depth(ci, cj)
if depth > 0:
collision_cost += collision_weight * depth
for p in placements:
dev = device_map[p.device_id]
hw, hd = p.rotated_bbox(dev)
overshoot = 0.0
overshoot += max(0.0, hw - p.x)
overshoot += max(0.0, (p.x + hw) - lab.width)
overshoot += max(0.0, hd - p.y)
overshoot += max(0.0, (p.y + hd) - lab.depth)
boundary_cost += boundary_weight * overshoot
return {
"collision": collision_cost,
"boundary": boundary_cost,
"total": collision_cost + boundary_cost,
"collision_weight": collision_weight,
"boundary_weight": boundary_weight,
}
def evaluate_constraints_breakdown(
devices: list[Device],
placements: list[Placement],
lab: Lab,
constraints: list[Constraint],
collision_checker: CollisionChecker,
reachability_checker: ReachabilityChecker | None = None,
) -> list[dict[str, Any]]:
"""与 evaluate_constraints 逻辑相同,但返回每条约束的分项明细。"""
device_map = {d.id: d for d in devices}
placement_map = {p.device_id: p for p in placements}
results = []
for c in constraints:
cost = _evaluate_single(
c, device_map, placement_map, lab, collision_checker, reachability_checker,
graduated=True,
)
results.append({
"name": _constraint_display_name(c),
"rule": c.rule_name,
"type": c.type,
"cost": cost,
"weight": c.weight,
})
return results
def _missing_reference_cost(
constraint: Constraint,
placement_map: dict[str, Placement],
*device_ids: str,
) -> float | None:
"""当约束引用不存在的设备时返回对应 cost。"""
missing = sorted({device_id for device_id in device_ids if device_id not in placement_map})
if not missing:
return None
logger.warning(
"Constraint %s references missing device IDs: %s",
constraint.rule_name,
", ".join(missing),
)
if constraint.type == "hard":
return math.inf
return 0.0
def _constraint_display_name(c: Constraint) -> str:
"""为约束生成可读的显示名称。"""
params = c.params
if c.rule_name in (
"distance_less_than", "distance_greater_than",
"minimize_distance", "maximize_distance",
):
return f"{c.rule_name}({params.get('device_a', '?')}, {params.get('device_b', '?')})"
if c.rule_name == "reachability":
return f"reachability({params.get('arm_id', '?')}, {params.get('target_device_id', '?')})"
if c.rule_name == "min_spacing":
return f"min_spacing(gap={params.get('min_gap', '?')})"
if c.rule_name == "prefer_orientation_mode":
return f"prefer_orientation_mode({params.get('mode', '?')})"
return c.rule_name
def _crossing_penalty(
opening_pt: tuple[float, float],
arm_nearest_pt: tuple[float, float],
arm_id: str,
target_id: str,
device_map: dict[str, Device],
placement_map: dict[str, Placement],
) -> float:
"""交叉惩罚:其他设备 OBB 遮挡 opening→arm 路径的长度加权 penalty。
Soft penalty权重 = DEFAULT_WEIGHT_DISTANCE * 穿过各遮挡设备 OBB 的线段长度之和。
始终生效(不论可达性是否通过),为 DE 提供清晰的梯度信号。
"""
cost = 0.0
for dev_id, p in placement_map.items():
if dev_id == arm_id or dev_id == target_id:
continue
dev = device_map.get(dev_id)
if dev is None:
continue
corners = obb_corners(p.x, p.y, dev.bbox[0], dev.bbox[1], p.theta)
crossing_len = segment_obb_intersection_length(opening_pt, arm_nearest_pt, corners)
cost += DEFAULT_WEIGHT_DISTANCE * crossing_len
return cost

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@@ -1,302 +0,0 @@
"""双源设备目录:从 uni-lab-assets 和 Uni-Lab-OS registry 加载设备。
数据流:
footprints.json (离线提取) + data.json (资产树) + registry device_mesh dirs
→ merge → Device 列表
footprints.json 由 extract_footprints.py 生成,包含碰撞包围盒、开口方向等。
"""
from __future__ import annotations
from collections import Counter
import json
import logging
from pathlib import Path
from .models import Device, Opening
logger = logging.getLogger(__name__)
# 默认路径(相对于本文件)
_THIS_DIR = Path(__file__).resolve().parent
_DEFAULT_FOOTPRINTS = _THIS_DIR / "footprints.json"
# 手动后备尺寸trimesh 不可用时)
KNOWN_SIZES: dict[str, tuple[float, float]] = {
"elite_cs66_arm": (0.20, 0.20),
"elite_cs612_arm": (0.20, 0.20),
"ot2": (0.62, 0.50),
"agilent_bravo": (0.80, 0.65),
"thermo_orbitor_rs2": (0.45, 0.55),
"hplc_station": (0.60, 0.50),
"1_3m_hamilton_table": (1.30, 0.75),
}
DEFAULT_BBOX: tuple[float, float] = (0.6, 0.4)
# ---------- footprints.json 加载 ----------
_footprints_cache: dict[str, dict] | None = None
def load_footprints(path: str | Path = _DEFAULT_FOOTPRINTS) -> dict[str, dict]:
"""加载 footprints.json 并缓存。"""
global _footprints_cache
if _footprints_cache is not None:
return _footprints_cache
p = Path(path)
if not p.exists():
logger.warning("footprints.json not found at %s", p)
_footprints_cache = {}
return _footprints_cache
with open(p) as f:
_footprints_cache = json.load(f)
logger.info("Loaded %d footprints from %s", len(_footprints_cache), p)
return _footprints_cache
def reset_footprints_cache() -> None:
"""清除缓存(测试用)。"""
global _footprints_cache
_footprints_cache = None
# ---------- 从 footprints 构建 Device ----------
def _footprint_to_device(
device_id: str,
fp: dict,
name: str = "",
models_url_prefix: str = "/models",
) -> Device:
"""从 footprints.json 条目创建 Device。"""
bbox = tuple(fp.get("bbox", DEFAULT_BBOX))
openings = [
Opening(direction=tuple(o["direction"]), label=o.get("label", ""))
for o in fp.get("openings", [])
]
model_file = fp.get("model_file", "")
model_path = f"{models_url_prefix}/{device_id}/{model_file}" if model_file else ""
model_type = fp.get("model_type", "")
thumb_file = fp.get("thumbnail_file", "")
thumbnail_url = f"{models_url_prefix}/{device_id}/{thumb_file}" if thumb_file else ""
return Device(
id=device_id,
name=name or device_id.replace("_", " ").title(),
bbox=bbox,
device_type="articulation" if "robot" in device_id or "arm" in device_id or "flex" in device_id else "static",
height=fp.get("height", 0.4),
origin_offset=tuple(fp.get("origin_offset", [0.0, 0.0])),
openings=openings,
source=fp.get("source", "manual"),
model_path=model_path,
model_type=model_type,
thumbnail_url=thumbnail_url,
)
# ---------- 从 data.json 加载 ----------
def load_devices_from_assets(
data_json_path: str | Path,
footprints: dict[str, dict] | None = None,
models_url_prefix: str = "/models",
) -> list[Device]:
"""从 uni-lab-assets 的 data.json 加载设备列表。
如果设备在 footprints 中有条目,使用真实尺寸;否则使用默认值。
"""
path = Path(data_json_path)
if not path.exists():
logger.warning("data.json not found at %s, returning empty list", path)
return []
if footprints is None:
footprints = load_footprints()
with open(path) as f:
data = json.load(f)
devices: list[Device] = []
_flatten_tree(data, devices, footprints, models_url_prefix)
return devices
def _flatten_tree(
nodes: list[dict],
result: list[Device],
footprints: dict[str, dict],
models_url_prefix: str,
) -> None:
"""递归遍历树形结构,提取叶节点为 Device。"""
for node in nodes:
if "children" in node:
_flatten_tree(node["children"], result, footprints, models_url_prefix)
elif "id" in node:
device_id = node["id"]
name = node.get("label", device_id)
if device_id in footprints:
dev = _footprint_to_device(
device_id, footprints[device_id], name, models_url_prefix
)
else:
bbox = KNOWN_SIZES.get(device_id, DEFAULT_BBOX)
dev = Device(id=device_id, name=name, bbox=bbox, source="assets")
result.append(dev)
# ---------- 从 registry 加载 ----------
def load_devices_from_registry(
device_mesh_dir: str | Path,
footprints: dict[str, dict] | None = None,
models_url_prefix: str = "/models",
) -> list[Device]:
"""从 Uni-Lab-OS device_mesh/devices/ 加载 registry 设备。"""
d = Path(device_mesh_dir)
if not d.exists():
logger.warning("Registry dir not found at %s", d)
return []
if footprints is None:
footprints = load_footprints()
devices: list[Device] = []
for entry in sorted(d.iterdir()):
if not entry.is_dir():
continue
device_id = entry.name
if device_id in footprints:
dev = _footprint_to_device(
device_id, footprints[device_id], models_url_prefix=models_url_prefix
)
dev.source = "registry"
else:
bbox = KNOWN_SIZES.get(device_id, DEFAULT_BBOX)
dev = Device(id=device_id, name=device_id.replace("_", " ").title(), bbox=bbox, source="registry")
devices.append(dev)
return devices
# ---------- 合并与去重 ----------
def merge_device_lists(
registry_devices: list[Device],
asset_devices: list[Device],
) -> list[Device]:
"""合并双源设备列表registry 优先。
对于同时存在于两个源的设备,使用 registry 条目的元数据,
但优先使用 assets 的 3D 模型路径和缩略图。
"""
merged: dict[str, Device] = {}
for dev in asset_devices:
merged[dev.id] = dev
for dev in registry_devices:
if dev.id in merged:
# registry 元数据优先,但保留 assets 的模型/缩略图
asset_dev = merged[dev.id]
dev.model_path = dev.model_path or asset_dev.model_path
dev.model_type = dev.model_type or asset_dev.model_type
dev.thumbnail_url = dev.thumbnail_url or asset_dev.thumbnail_url
if dev.bbox == DEFAULT_BBOX and asset_dev.bbox != DEFAULT_BBOX:
dev.bbox = asset_dev.bbox
dev.height = asset_dev.height
dev.origin_offset = asset_dev.origin_offset
dev.openings = asset_dev.openings
dev.source = "registry"
merged[dev.id] = dev
return list(merged.values())
# ---------- 统一解析器 ----------
def resolve_device(
device_id: str,
footprints: dict[str, dict] | None = None,
models_url_prefix: str = "/models",
) -> Device | None:
"""按 ID 查找单个设备。先查 footprints再查 KNOWN_SIZES。"""
if footprints is None:
footprints = load_footprints()
if device_id in footprints:
return _footprint_to_device(
device_id, footprints[device_id], models_url_prefix=models_url_prefix
)
if device_id in KNOWN_SIZES:
bbox = KNOWN_SIZES[device_id]
return Device(id=device_id, name=device_id.replace("_", " ").title(), bbox=bbox, source="manual")
return None
# ---------- 向后兼容 ----------
def create_devices_from_list(
device_specs: list[dict],
) -> list[Device]:
"""从 API 请求中的设备列表创建 Device 对象(向后兼容)。
Args:
device_specs: [{"id": str, "name": str, "size": [w, d], "uuid": str}, ...]
size 可选,缺失时使用 footprints 或默认值。
"""
footprints = load_footprints()
devices = []
catalog_counts = Counter(spec["id"] for spec in device_specs)
catalog_seen: Counter[str] = Counter()
for spec in device_specs:
catalog_id = spec["id"]
catalog_seen[catalog_id] += 1
instance_idx = catalog_seen[catalog_id]
if catalog_counts[catalog_id] > 1 and instance_idx > 1:
dev_id = f"{catalog_id}#{instance_idx}"
else:
dev_id = catalog_id
size = spec.get("size")
if size:
bbox = (float(size[0]), float(size[1]))
elif catalog_id in footprints:
bbox = tuple(footprints[catalog_id].get("bbox", DEFAULT_BBOX))
else:
bbox = KNOWN_SIZES.get(catalog_id, DEFAULT_BBOX)
openings = []
if catalog_id in footprints:
openings = [
Opening(direction=tuple(o["direction"]), label=o.get("label", ""))
for o in footprints[catalog_id].get("openings", [])
]
devices.append(
Device(
id=dev_id,
name=spec.get("name", catalog_id),
bbox=bbox,
device_type=spec.get("device_type", "static"),
openings=openings,
uuid=spec.get("uuid", ""),
)
)
return devices

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@@ -1,559 +0,0 @@
"""从 STL/GLB 网格提取设备足迹(碰撞包围盒)。
运行方式:
conda activate phase3
python -m layout_optimizer.extract_footprints
输出 footprints.json 供 device_catalog.py 和 2D 规划器使用。
GLB root node rotation:
每个设备的 GLB 文件包含根节点旋转四元数,定义 STL 原生坐标到 glTF Y-up
约定的轴映射。extract_one_device() 读取 GLB JSON提取旋转矩阵
应用到 STL 包围盒后按 glTF 约定提取 2D 足迹 (X=width, Z=depth, Y=height)。
GLB scale 不应用——STL 文件已是米制坐标。
"""
from __future__ import annotations
import argparse
import json
import logging
import math
import os
import re
import struct
import xml.etree.ElementTree as ET
from pathlib import Path
logger = logging.getLogger(__name__)
# 测试设备的开口方向(手动标注)
# direction 为设备局部坐标系中的单位向量,[0, -1] 表示设备正前方
MANUAL_OPENINGS: dict[str, list[dict]] = {
"agilent_bravo": [{"direction": [0, -1], "label": "front_plate_slot"}],
"opentrons_liquid_handler": [{"direction": [0, -1], "label": "front_deck"}],
"opentrons_flex": [{"direction": [0, -1], "label": "front_deck"}],
"thermo_orbitor_rs2_hotel": [{"direction": [0, -1], "label": "front_door"}],
"hamilton_star": [{"direction": [0, -1], "label": "front_deck"}],
"tecan_spark_plate_reader": [{"direction": [0, -1], "label": "front_slot"}],
"highres_bio_plate_hotel_12": [{"direction": [0, -1], "label": "front_shelf"}],
"beckman_coulter_orbital_shaker_alp": [],
"liconic_str44_incubator": [{"direction": [0, -1], "label": "front_door"}],
"elite_robot": [], # 机械臂,无开口
}
# 手动尺寸后备trimesh 提取失败时使用)
FALLBACK_SIZES: dict[str, tuple[float, float, float]] = {
"elite_robot": (0.20, 0.20, 0.10),
"elite_cs66_arm": (0.20, 0.20, 0.10),
"elite_cs612_arm": (0.20, 0.20, 0.10),
}
def extract_openings_from_xacro(
xacro_path: Path,
bbox_center_xy: tuple[float, float],
bbox_size_xy: tuple[float, float],
) -> list[dict]:
"""从 XACRO 文件自动提取设备开口方向。
解析 fixed joint 中包含 "socket" 的关节,计算其 XY 质心,与包围盒中心比较,
映射到最近的基本方向。
Args:
xacro_path: modal.xacro 文件路径
bbox_center_xy: 包围盒 XY 中心 (cx, cy)
bbox_size_xy: 包围盒 XY 尺寸 (w, d)
Returns:
[{"direction": [dx, dy], "label": "auto_xacro"}] 或 []
"""
# --- 方法1: ElementTree 解析(忽略 xacro 命名空间) ---
socket_positions: list[tuple[float, float]] = []
try:
xacro_text = xacro_path.read_text(encoding="utf-8", errors="replace")
# 去掉 xacro 命名空间前缀,避免 ElementTree 解析失败
xacro_text_clean = re.sub(r'\bxacro:', '', xacro_text)
root = ET.fromstring(xacro_text_clean)
for joint in root.iter("joint"):
joint_name = joint.get("name", "")
joint_type = joint.get("type", "")
if "socket" not in joint_name.lower():
continue
if joint_type != "fixed":
continue
origin = joint.find("origin")
if origin is None:
continue
xyz_str = origin.get("xyz", "")
if not xyz_str:
continue
parts = xyz_str.split()
if len(parts) < 2:
continue
try:
x = float(parts[0])
y = float(parts[1])
socket_positions.append((x, y))
except ValueError:
continue
except ET.ParseError as e:
logger.debug("ElementTree parse error for %s: %s — falling back to regex", xacro_path, e)
# --- 方法2: 正则表达式后备(当 ElementTree 失败或无结果时) ---
if not socket_positions:
try:
xacro_text = xacro_path.read_text(encoding="utf-8", errors="replace")
# 匹配包含 "socket" 的 joint 块,提取 origin xyz
joint_blocks = re.findall(
r'<joint\s[^>]*name=["\'][^"\']*socket[^"\']*["\'][^>]*>.*?</joint>',
xacro_text,
flags=re.IGNORECASE | re.DOTALL,
)
for block in joint_blocks:
# 只处理 fixed 类型
if 'type="fixed"' not in block and "type='fixed'" not in block:
continue
xyz_match = re.search(r'<origin[^>]*xyz=["\']([^"\']+)["\']', block)
if not xyz_match:
continue
parts = xyz_match.group(1).split()
if len(parts) < 2:
continue
try:
x = float(parts[0])
y = float(parts[1])
socket_positions.append((x, y))
except ValueError:
continue
except Exception as e:
logger.debug("Regex fallback also failed for %s: %s", xacro_path, e)
if not socket_positions:
return []
# 计算 socket XY 质心
cx_sock = sum(p[0] for p in socket_positions) / len(socket_positions)
cy_sock = sum(p[1] for p in socket_positions) / len(socket_positions)
# 方向向量:从包围盒中心指向 socket 质心
dx = cx_sock - bbox_center_xy[0]
dy = cy_sock - bbox_center_xy[1]
# 如果 socket 质心非常靠近包围盒中心(<5% 尺寸),判断为顶部装载
threshold = 0.05 * max(bbox_size_xy[0], bbox_size_xy[1], 1e-6)
if math.hypot(dx, dy) < threshold:
logger.debug(
"%s: socket centroid too close to bbox center (dist=%.4f, threshold=%.4f) → top-loading",
xacro_path.parent.name,
math.hypot(dx, dy),
threshold,
)
return []
# 映射到最近基本方向
# socket 质心指示交互区在设备哪一侧,而 opening direction 是从该面
# 向外的法线方向(与质心偏移同向),这里的 dx/dy 已经是从包围盒中心
# 指向 socket 区域的方向,即 opening 朝外的方向
# 注意:在 uni-lab-assets 中,大多数设备 front 在 Y=0 而 body 在 -Y
# 所以 socket 集中在 +Y 侧(靠近 Y=0 前端bbox 中心在 -Y/2。
# 方向 center→socket = +Y但 "opening faces front" 在手动标注中
# 写作 [0, -1](法线向外=向操作者方向)。
# 因此需要取反opening direction = -(center→socket)
if abs(dx) >= abs(dy):
cardinal = [-1, 0] if dx > 0 else [1, 0]
else:
cardinal = [0, -1] if dy > 0 else [0, 1]
logger.debug(
"%s: %d socket joints → centroid=(%.3f, %.3f) dir=%s",
xacro_path.parent.name,
len(socket_positions),
cx_sock,
cy_sock,
cardinal,
)
return [{"direction": cardinal, "label": "auto_xacro"}]
def _find_mesh_files(device_dir: Path) -> list[Path]:
"""查找设备目录中的所有 STL/GLB 网格文件。"""
mesh_files: list[Path] = []
meshes_dir = device_dir / "meshes"
if not meshes_dir.exists():
return mesh_files
# uni-lab-assets 结构: meshes/*.stl, meshes/*.glb
for f in meshes_dir.iterdir():
if f.suffix.lower() in (".stl", ".glb"):
mesh_files.append(f)
# registry 结构: meshes/<variant>/collision/*.stl
if not mesh_files:
for variant_dir in meshes_dir.iterdir():
if variant_dir.is_dir():
collision_dir = variant_dir / "collision"
if collision_dir.exists():
for f in collision_dir.iterdir():
if f.suffix.lower() == ".stl":
mesh_files.append(f)
if mesh_files:
break # 使用找到的第一个变体
return sorted(mesh_files)
def _find_best_model_file(device_dir: Path) -> tuple[str, str]:
"""找到最佳可展示的模型文件。优先 GLB > STL。
Returns:
(relative_path, model_type) e.g. ("meshes/0_base.glb", "gltf")
"""
meshes_dir = device_dir / "meshes"
if not meshes_dir.exists():
return "", ""
glbs = sorted(meshes_dir.glob("*.glb"))
if glbs:
return f"meshes/{glbs[0].name}", "gltf"
stls = sorted(f for f in meshes_dir.glob("*.stl") if f.suffix == ".stl")
if not stls:
stls = sorted(f for f in meshes_dir.glob("*.STL"))
if stls:
return f"meshes/{stls[0].name}", "stl"
return "", ""
def _find_thumbnail(device_dir: Path) -> str:
"""查找设备目录中的第一个 PNG 缩略图。"""
pngs = sorted(device_dir.glob("*.png"))
if pngs:
return pngs[0].name
return ""
def _read_glb_json(glb_path: Path) -> dict | None:
"""Read the JSON chunk from a GLB (Binary glTF) file.
GLB structure: 12-byte header + chunks. Chunk 0 is JSON.
Returns parsed dict or None on failure.
"""
try:
with open(glb_path, "rb") as f:
header = f.read(12)
if len(header) < 12:
return None
magic, version, length = struct.unpack("<III", header)
if magic != 0x46546C67: # 'glTF'
return None
chunk_header = f.read(8)
if len(chunk_header) < 8:
return None
chunk_length, chunk_type = struct.unpack("<II", chunk_header)
if chunk_type != 0x4E4F534A: # 'JSON'
return None
json_bytes = f.read(chunk_length)
return json.loads(json_bytes)
except Exception as e:
logger.debug("Failed to read GLB JSON from %s: %s", glb_path, e)
return None
def _quat_to_matrix(q: list[float]) -> list[list[float]]:
"""Convert quaternion [x, y, z, w] to 3×3 rotation matrix."""
x, y, z, w = q
return [
[1 - 2*(y*y + z*z), 2*(x*y - z*w), 2*(x*z + y*w)],
[ 2*(x*y + z*w), 1 - 2*(x*x + z*z), 2*(y*z - x*w)],
[ 2*(x*z - y*w), 2*(y*z + x*w), 1 - 2*(x*x + y*y)],
]
def _get_glb_root_rotation(device_dir: Path) -> list[list[float]] | None:
"""Extract root node rotation matrix from the first GLB in device_dir/meshes/.
Only rotation is extracted — GLB scale is NOT applied because STL files
are already in meters while GLB scale converts GLB mesh units (often mm)
to scene units. Since we read STL directly, scale is irrelevant.
Returns 3×3 rotation matrix or None if no GLB or no rotation found.
"""
meshes_dir = device_dir / "meshes"
if not meshes_dir.exists():
return None
glbs = sorted(meshes_dir.glob("*.glb"))
if not glbs:
return None
gltf = _read_glb_json(glbs[0])
if gltf is None:
return None
nodes = gltf.get("nodes", [])
if not nodes:
return None
root = nodes[0]
rotation = root.get("rotation")
if rotation is None:
return None
# Skip identity quaternion [0,0,0,1]
x, y, z, w = rotation
if abs(x) < 1e-9 and abs(y) < 1e-9 and abs(z) < 1e-9 and abs(w - 1.0) < 1e-9:
return None
return _quat_to_matrix(rotation)
def _apply_rotation_to_bbox(
stl_min: list[float], stl_max: list[float],
rot: list[list[float]],
) -> tuple[list[float], list[float]]:
"""Apply rotation to an axis-aligned bounding box.
Transforms all 8 corners of the STL AABB through rotation,
then computes the new AABB in glTF space.
"""
corners = []
for x in (stl_min[0], stl_max[0]):
for y in (stl_min[1], stl_max[1]):
for z in (stl_min[2], stl_max[2]):
tx = rot[0][0]*x + rot[0][1]*y + rot[0][2]*z
ty = rot[1][0]*x + rot[1][1]*y + rot[1][2]*z
tz = rot[2][0]*x + rot[2][1]*y + rot[2][2]*z
corners.append((tx, ty, tz))
xs = [c[0] for c in corners]
ys = [c[1] for c in corners]
zs = [c[2] for c in corners]
return [min(xs), min(ys), min(zs)], [max(xs), max(ys), max(zs)]
def extract_one_device(device_dir: Path) -> dict | None:
"""提取单个设备的足迹信息。"""
try:
import trimesh
except ImportError:
logger.error("trimesh not installed. Run: pip install trimesh")
return None
mesh_files = _find_mesh_files(device_dir)
if not mesh_files:
return None
# 加载所有网格部件并计算联合包围盒
meshes = []
for f in mesh_files:
try:
m = trimesh.load(str(f), force="mesh")
if hasattr(m, "bounds") and m.bounds is not None:
meshes.append(m)
except Exception as e:
logger.warning("Failed to load %s: %s", f, e)
if not meshes:
return None
if len(meshes) == 1:
combined = meshes[0]
else:
combined = trimesh.util.concatenate(meshes)
bounds = combined.bounds
stl_min = [float(bounds[0][i]) for i in range(3)]
stl_max = [float(bounds[1][i]) for i in range(3)]
# 应用 GLB 根节点旋转到 STL 包围盒scale 不应用 — STL 已是米制)
# glTF 约定: X=right, Y=up, Z=forward → 2D 足迹取 X 和 Z, 高度取 Y
rot = _get_glb_root_rotation(device_dir)
if rot is not None:
t_min, t_max = _apply_rotation_to_bbox(stl_min, stl_max, rot)
t_size = [t_max[i] - t_min[i] for i in range(3)]
t_center = [(t_min[i] + t_max[i]) / 2 for i in range(3)]
# glTF Y-up: X=width, Z=depth, Y=height
bbox_w = round(t_size[0], 4)
bbox_d = round(t_size[2], 4)
height = round(t_size[1], 4)
origin_offset = [round(t_center[0], 4), round(t_center[2], 4)]
logger.debug(
"%s: GLB rotation applied → bbox=[%.3f, %.3f] height=%.3f",
device_dir.name, bbox_w, bbox_d, height,
)
else:
# 无 GLB 或 identity rotation → 沿用原始 STL 坐标 (X=width, Y=depth, Z=height)
size = [stl_max[i] - stl_min[i] for i in range(3)]
center = [(stl_min[i] + stl_max[i]) / 2 for i in range(3)]
bbox_w = round(size[0], 4)
bbox_d = round(size[1], 4)
height = round(size[2], 4)
origin_offset = [round(center[0], 4), round(center[1], 4)]
model_file, model_type = _find_best_model_file(device_dir)
thumbnail_file = _find_thumbnail(device_dir)
device_id = device_dir.name
# 确定 openings手动标注优先否则尝试从 XACRO 自动提取
# 注意XACRO socket 坐标是 STL 原生坐标系,这里传入变换后的 bbox
if device_id in MANUAL_OPENINGS:
openings = MANUAL_OPENINGS[device_id]
else:
xacro_path = device_dir / "modal.xacro"
if xacro_path.exists():
openings = extract_openings_from_xacro(
xacro_path,
bbox_center_xy=(origin_offset[0], origin_offset[1]),
bbox_size_xy=(bbox_w, bbox_d),
)
else:
openings = []
result: dict = {
"bbox": [bbox_w, bbox_d],
"height": height,
"origin_offset": origin_offset,
"model_file": model_file,
"model_type": model_type,
"thumbnail_file": thumbnail_file,
"openings": openings,
}
return result
def extract_all(
assets_dir: Path | None = None,
registry_dir: Path | None = None,
device_ids: list[str] | None = None,
) -> dict[str, dict]:
"""提取所有(或指定)设备的足迹。
Args:
assets_dir: uni-lab-assets/device_models/ 路径
registry_dir: Uni-Lab-OS/unilabos/device_mesh/devices/ 路径
device_ids: 仅提取指定设备None = 全部扫描)
Returns:
{device_id: footprint_dict}
"""
results: dict[str, dict] = {}
dirs_to_scan: list[tuple[Path, str]] = []
if assets_dir and assets_dir.exists():
for d in sorted(assets_dir.iterdir()):
if d.is_dir() and (device_ids is None or d.name in device_ids):
dirs_to_scan.append((d, "assets"))
if registry_dir and registry_dir.exists():
for d in sorted(registry_dir.iterdir()):
if d.is_dir() and (device_ids is None or d.name in device_ids):
if d.name not in results: # assets 已有的不重复扫描
dirs_to_scan.append((d, "registry"))
for device_dir, source in dirs_to_scan:
device_id = device_dir.name
if device_id in results:
continue
footprint = extract_one_device(device_dir)
if footprint:
footprint["source"] = source
results[device_id] = footprint
logger.info(
"Extracted %s: bbox=%s height=%.3f source=%s",
device_id,
footprint["bbox"],
footprint["height"],
source,
)
# 统计自动提取的 openings 数量
auto_xacro_count = sum(
1
for fp in results.values()
if any(o.get("label") == "auto_xacro" for o in fp.get("openings", []))
)
logger.info(
"Auto-extracted openings from XACRO for %d / %d devices",
auto_xacro_count,
len(results),
)
# 手动后备
for dev_id, (w, d, h) in FALLBACK_SIZES.items():
if dev_id not in results:
results[dev_id] = {
"bbox": [w, d],
"height": h,
"origin_offset": [0.0, 0.0],
"model_file": "",
"model_type": "",
"thumbnail_file": "",
"openings": MANUAL_OPENINGS.get(dev_id, []),
"source": "manual",
}
return results
def main() -> None:
parser = argparse.ArgumentParser(
description="Extract device footprints from STL/GLB meshes"
)
parser.add_argument(
"--assets-dir",
type=Path,
default=Path(__file__).resolve().parent.parent / "uni-lab-assets" / "device_models",
help="Path to uni-lab-assets/device_models/",
)
parser.add_argument(
"--registry-dir",
type=Path,
default=Path(__file__).resolve().parent / "Uni-Lab-OS" / "unilabos" / "device_mesh" / "devices",
help="Path to Uni-Lab-OS device_mesh/devices/",
)
parser.add_argument(
"--output",
type=Path,
default=Path(__file__).resolve().parent / "footprints.json",
help="Output JSON path",
)
parser.add_argument(
"--devices",
nargs="*",
default=None,
help="Only extract these device IDs (default: all)",
)
parser.add_argument("-v", "--verbose", action="store_true")
args = parser.parse_args()
logging.basicConfig(
level=logging.DEBUG if args.verbose else logging.INFO,
format="%(levelname)s: %(message)s",
)
logger.info("Assets dir: %s (exists=%s)", args.assets_dir, args.assets_dir.exists())
logger.info("Registry dir: %s (exists=%s)", args.registry_dir, args.registry_dir.exists())
results = extract_all(
assets_dir=args.assets_dir,
registry_dir=args.registry_dir,
device_ids=args.devices,
)
with open(args.output, "w") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
logger.info("Wrote %d devices to %s", len(results), args.output)
if __name__ == "__main__":
main()

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@@ -1,187 +0,0 @@
"""
Generate a YAML registry file for all devices in uni-lab-assets that don't
already have a registry entry (identified by model.mesh value).
Output: Uni-Lab-OS/unilabos/registry/devices/asset_models.yaml
"""
import json
from pathlib import Path
import yaml
# ---------------------------------------------------------------------------
# Paths (resolved relative to this script's location)
# ---------------------------------------------------------------------------
REPO_ROOT = Path(__file__).parent.parent
ASSETS_DIR = REPO_ROOT.parent / "uni-lab-assets" / "device_models"
REGISTRY_DIR = REPO_ROOT / "Uni-Lab-OS" / "unilabos" / "registry" / "devices"
OUTPUT_FILE = REGISTRY_DIR / "asset_models.yaml"
OSS_BASE = (
"https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices"
)
CONTAINER_CLASS = (
"unilabos.devices.resource_container.container:HotelContainer"
)
# ---------------------------------------------------------------------------
# Step 1 — collect mesh names already present in the registry
# ---------------------------------------------------------------------------
def collect_registered_meshes() -> set[str]:
"""Return the set of mesh values found in all existing registry YAML files."""
registered: set[str] = set()
for yaml_file in REGISTRY_DIR.glob("*.yaml"):
# Skip the output file itself so the script is idempotent
if yaml_file == OUTPUT_FILE:
continue
try:
with yaml_file.open("r", encoding="utf-8") as fh:
data = yaml.safe_load(fh)
except Exception as exc:
print(f" [warn] Could not parse {yaml_file.name}: {exc}")
continue
if not isinstance(data, dict):
continue
for _key, entry in data.items():
if not isinstance(entry, dict):
continue
model = entry.get("model")
if isinstance(model, dict):
mesh = model.get("mesh")
if mesh:
registered.add(str(mesh))
return registered
# ---------------------------------------------------------------------------
# Step 2 — scan uni-lab-assets/device_models/
# ---------------------------------------------------------------------------
def scan_asset_devices() -> list[dict]:
"""
Return a list of device dicts for every subfolder that has a modal.xacro.
Each dict has keys: folder_name, description.
"""
devices = []
if not ASSETS_DIR.is_dir():
raise FileNotFoundError(f"Assets directory not found: {ASSETS_DIR}")
for device_dir in sorted(ASSETS_DIR.iterdir()):
if not device_dir.is_dir():
continue
folder_name = device_dir.name
# modal.xacro is required
if not (device_dir / "modal.xacro").exists():
continue
# Read optional meta.json
description = folder_name
meta_path = device_dir / "meta.json"
if meta_path.exists():
try:
with meta_path.open("r", encoding="utf-8") as fh:
meta = json.load(fh)
# Use name field if present; otherwise fall back to folder name
description = meta.get("name", folder_name)
except Exception as exc:
print(f" [warn] Could not parse {meta_path}: {exc}")
devices.append(
{
"folder_name": folder_name,
"description": description,
}
)
return devices
# ---------------------------------------------------------------------------
# Step 3 — build registry entry for a single device
# ---------------------------------------------------------------------------
def build_entry(folder_name: str, description: str) -> dict:
return {
"category": ["asset_model"],
"class": {
"action_value_mappings": {},
"module": CONTAINER_CLASS,
"status_types": {},
"type": "python",
},
"config_info": [],
"description": description,
"handles": [],
"icon": "",
"init_param_schema": {
"config": {
"properties": {},
"required": [],
"type": "object",
},
"data": {
"properties": {},
"required": [],
"type": "object",
},
},
"model": {
"mesh": folder_name,
"path": f"{OSS_BASE}/{folder_name}/macro_device.xacro",
"type": "device",
},
"version": "1.0.0",
}
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main() -> None:
print("Scanning existing registry for registered meshes...")
registered_meshes = collect_registered_meshes()
print(f" Found {len(registered_meshes)} already-registered mesh(es).")
print(f"\nScanning asset devices in: {ASSETS_DIR}")
all_devices = scan_asset_devices()
print(f" Found {len(all_devices)} device folder(s) with modal.xacro.")
registry: dict[str, dict] = {}
skipped = 0
generated = 0
for device in all_devices:
folder_name = device["folder_name"]
if folder_name in registered_meshes:
skipped += 1
continue
key = f"asset_model.{folder_name}"
registry[key] = build_entry(folder_name, device["description"])
generated += 1
print(f"\nWriting {generated} new entr(ies) to: {OUTPUT_FILE}")
OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
with OUTPUT_FILE.open("w", encoding="utf-8") as fh:
yaml.dump(
registry,
fh,
default_flow_style=False,
allow_unicode=True,
sort_keys=False,
)
print("\n--- Summary ---")
print(f" Total devices found (with modal.xacro): {len(all_devices)}")
print(f" Already registered (skipped): {skipped}")
print(f" Newly generated: {generated}")
if __name__ == "__main__":
main()

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@@ -1,373 +0,0 @@
"""意图解释器:将语义化意图翻译为 Constraint 列表。"""
from __future__ import annotations
import itertools
from collections.abc import Callable
from dataclasses import dataclass, field
from .constraints import PRIORITY_MULTIPLIERS
from .models import Constraint, Intent
# 优先级权重映射
_PRIORITY_WEIGHTS: dict[str, float] = {"low": 1.0, "medium": 3.0, "high": 8.0}
_DEFAULT_WEIGHT = _PRIORITY_WEIGHTS["medium"]
def _priority_key(priority: str) -> str:
"""将 intent priority 映射到 constraint 权重等级。"""
return "normal" if priority == "medium" else priority
def _final_weight(base_weight: float, priority: str) -> float:
"""在解释阶段直接烘焙优先级乘数。"""
return base_weight * PRIORITY_MULTIPLIERS.get(priority, 1.0)
@dataclass
class InterpretResult:
"""意图解释结果。"""
constraints: list[Constraint] = field(default_factory=list)
translations: list[dict] = field(default_factory=list)
errors: list[str] = field(default_factory=list)
workflow_edges: list[list[str]] = field(default_factory=list)
def _handle_reachable_by(intent: Intent, result: InterpretResult) -> None:
"""reachable_by机械臂必须能到达指定设备列表。"""
arm = intent.params.get("arm")
targets = intent.params.get("targets", [])
if arm is None:
result.errors.append(f"reachable_by: 缺少必要参数 'arm'")
return
if not targets:
result.errors.append(f"reachable_by: 参数 'targets' 不能为空")
return
generated: list[dict] = []
for target in targets:
c = Constraint(
type="hard",
rule_name="reachability",
params={"arm_id": arm, "target_device_id": target},
weight=_final_weight(1.0, "critical"),
)
result.constraints.append(c)
generated.append({"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight})
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": generated,
"explanation": f"机械臂 '{arm}' 需要能够到达 {len(targets)} 个目标设备",
})
def _handle_close_together(intent: Intent, result: InterpretResult) -> None:
"""close_together设备组内两两最小化距离。"""
devices: list[str] = intent.params.get("devices", [])
priority: str = intent.params.get("priority", "medium")
if len(devices) < 2:
result.errors.append(f"close_together: 参数 'devices' 至少需要 2 个设备,当前 {len(devices)}")
return
weight = _final_weight(
_PRIORITY_WEIGHTS.get(priority, _DEFAULT_WEIGHT),
_priority_key(priority),
)
generated: list[dict] = []
for dev_a, dev_b in itertools.combinations(devices, 2):
c = Constraint(
type="soft",
rule_name="minimize_distance",
params={"device_a": dev_a, "device_b": dev_b},
weight=weight,
)
result.constraints.append(c)
generated.append({"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight})
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": generated,
"explanation": f"设备组 {devices} 应尽量靠近(优先级: {priority}",
})
def _handle_far_apart(intent: Intent, result: InterpretResult) -> None:
"""far_apart设备组内两两最大化距离。"""
devices: list[str] = intent.params.get("devices", [])
priority: str = intent.params.get("priority", "medium")
if len(devices) < 2:
result.errors.append(f"far_apart: 参数 'devices' 至少需要 2 个设备,当前 {len(devices)}")
return
weight = _final_weight(
_PRIORITY_WEIGHTS.get(priority, _DEFAULT_WEIGHT),
_priority_key(priority),
)
generated: list[dict] = []
for dev_a, dev_b in itertools.combinations(devices, 2):
c = Constraint(
type="soft",
rule_name="maximize_distance",
params={"device_a": dev_a, "device_b": dev_b},
weight=weight,
)
result.constraints.append(c)
generated.append({"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight})
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": generated,
"explanation": f"设备组 {devices} 应尽量分散(优先级: {priority}",
})
def _handle_max_distance(intent: Intent, result: InterpretResult) -> None:
"""max_distance两设备间距不超过指定值。"""
device_a = intent.params.get("device_a")
device_b = intent.params.get("device_b")
distance = intent.params.get("distance")
if device_a is None or device_b is None or distance is None:
result.errors.append(
f"max_distance: 缺少必要参数,需要 'device_a''device_b''distance'"
f"当前: device_a={device_a}, device_b={device_b}, distance={distance}"
)
return
c = Constraint(
type="hard",
rule_name="distance_less_than",
params={"device_a": device_a, "device_b": device_b, "distance": distance},
weight=_final_weight(1.0, "normal"),
)
result.constraints.append(c)
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": [{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}],
"explanation": f"设备 '{device_a}''{device_b}' 之间的距离不得超过 {distance}",
})
def _handle_min_distance(intent: Intent, result: InterpretResult) -> None:
"""min_distance两设备间距不小于指定值。"""
device_a = intent.params.get("device_a")
device_b = intent.params.get("device_b")
distance = intent.params.get("distance")
if device_a is None or device_b is None or distance is None:
result.errors.append(
f"min_distance: 缺少必要参数,需要 'device_a''device_b''distance'"
f"当前: device_a={device_a}, device_b={device_b}, distance={distance}"
)
return
c = Constraint(
type="hard",
rule_name="distance_greater_than",
params={"device_a": device_a, "device_b": device_b, "distance": distance},
weight=_final_weight(1.0, "normal"),
)
result.constraints.append(c)
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": [{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}],
"explanation": f"设备 '{device_a}''{device_b}' 之间的距离不得小于 {distance}",
})
def _handle_min_spacing(intent: Intent, result: InterpretResult) -> None:
"""min_spacing所有设备之间的最小间隙。"""
min_gap: float = intent.params.get("min_gap", 0.3)
c = Constraint(
type="hard",
rule_name="min_spacing",
params={"min_gap": min_gap},
weight=_final_weight(1.0, "high"),
)
result.constraints.append(c)
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": [{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}],
"explanation": f"所有设备之间至少保持 {min_gap} 米的间隙",
})
def _handle_face_outward(intent: Intent, result: InterpretResult) -> None:
"""face_outward设备朝向偏好为向外。"""
c = Constraint(
type="soft",
rule_name="prefer_orientation_mode",
params={"mode": "outward"},
weight=_final_weight(1.0, "low"),
)
result.constraints.append(c)
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": [{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}],
"explanation": "设备开口偏好朝向实验室外侧",
})
def _handle_face_inward(intent: Intent, result: InterpretResult) -> None:
"""face_inward设备朝向偏好为向内。"""
c = Constraint(
type="soft",
rule_name="prefer_orientation_mode",
params={"mode": "inward"},
weight=_final_weight(1.0, "low"),
)
result.constraints.append(c)
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": [{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}],
"explanation": "设备开口偏好朝向实验室内侧",
})
def _handle_align_cardinal(intent: Intent, result: InterpretResult) -> None:
"""align_cardinal设备偏好对齐到主轴方向。"""
c = Constraint(
type="soft",
rule_name="prefer_aligned",
params={},
weight=_final_weight(1.0, "low"),
)
result.constraints.append(c)
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": [{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}],
"explanation": "设备偏好与实验室主轴对齐0°/90°/180°/270°",
})
def _handle_keep_adjacent(intent: Intent, result: InterpretResult) -> None:
"""keep_adjacent两个设备保持相邻同 close_together 逻辑,支持 priority 映射)。"""
devices: list[str] = intent.params.get("devices", [])
priority: str = intent.params.get("priority", "medium")
if len(devices) < 2:
result.errors.append(f"keep_adjacent: 参数 'devices' 至少需要 2 个设备,当前 {len(devices)}")
return
weight = _final_weight(
_PRIORITY_WEIGHTS.get(priority, _DEFAULT_WEIGHT),
_priority_key(priority),
)
generated: list[dict] = []
for dev_a, dev_b in itertools.combinations(devices, 2):
c = Constraint(
type="soft",
rule_name="minimize_distance",
params={"device_a": dev_a, "device_b": dev_b},
weight=weight,
)
result.constraints.append(c)
generated.append({"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight})
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": generated,
"explanation": f"设备组 {devices} 应保持相邻(优先级: {priority}",
})
def _handle_workflow_hint(intent: Intent, result: InterpretResult) -> None:
"""workflow_hint工作流顺序暗示相邻步骤设备靠近。"""
workflow: str = intent.params.get("workflow", "")
devices: list[str] = intent.params.get("devices", [])
if len(devices) < 2:
result.errors.append(
f"workflow_hint: 参数 'devices' 至少需要 2 个设备,当前 {len(devices)}"
)
return
generated: list[dict] = []
for dev_a, dev_b in zip(devices[:-1], devices[1:]):
c = Constraint(
type="soft",
rule_name="minimize_distance",
params={"device_a": dev_a, "device_b": dev_b},
weight=_final_weight(1.0, "normal"),
)
result.constraints.append(c)
generated.append({"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight})
result.workflow_edges.append([dev_a, dev_b])
result.translations.append({
"source_intent": intent.intent,
"source_description": intent.description,
"source_params": intent.params,
"generated_constraints": generated,
"explanation": f"工作流 '{workflow}' 中相邻步骤设备应靠近",
"confidence": "low",
})
# 意图处理器分发表
_HANDLERS: dict[str, Callable[[Intent, InterpretResult], None]] = {
"reachable_by": _handle_reachable_by,
"close_together": _handle_close_together,
"far_apart": _handle_far_apart,
"max_distance": _handle_max_distance,
"min_distance": _handle_min_distance,
"min_spacing": _handle_min_spacing,
"face_outward": _handle_face_outward,
"face_inward": _handle_face_inward,
"align_cardinal": _handle_align_cardinal,
"keep_adjacent": _handle_keep_adjacent,
"workflow_hint": _handle_workflow_hint,
}
def interpret_intents(intents: list[Intent]) -> InterpretResult:
"""将意图列表翻译为约束列表。
Args:
intents: 语义化意图列表(通常由 LLM 生成)
Returns:
InterpretResult包含约束、翻译记录、错误信息和工作流边
"""
result = InterpretResult()
for intent in intents:
handler = _HANDLERS.get(intent.intent)
if handler is None:
result.errors.append(f"未知意图类型: '{intent.intent}',跳过处理")
continue
handler(intent, result)
return result

View File

@@ -1,41 +0,0 @@
"""Protocol 接口定义,隔离 ROS 依赖。
开发阶段使用 mock_checkers.py 中的 Mock 实现,
集成阶段替换为 ros_checkers.py 中的 MoveIt2 / IKFast 实现。
"""
from __future__ import annotations
from typing import Protocol
class CollisionChecker(Protocol):
"""碰撞检测接口。"""
def check(self, placements: list[dict]) -> list[tuple[str, str]]:
"""返回碰撞设备对列表。
Args:
placements: [{"id": str, "bbox": (w, d), "pos": (x, y, θ)}, ...]
Returns:
[("device_a", "device_b"), ...] 存在碰撞的设备对
"""
...
class ReachabilityChecker(Protocol):
"""可达性检测接口。"""
def is_reachable(self, arm_id: str, arm_pose: dict, target: dict) -> bool:
"""判断机械臂在给定位姿下能否到达目标点。
Args:
arm_id: 机械臂设备 ID
arm_pose: {"x": float, "y": float, "theta": float}
target: {"x": float, "y": float, "z": float}
Returns:
True 如果可达
"""
...

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@@ -1,49 +0,0 @@
"""解析实验室平面图 JSON。
简单格式:
{
"width": 6.0,
"depth": 4.0,
"obstacles": [
{"x": 2.0, "y": 0.0, "width": 0.1, "depth": 1.0}
]
}
"""
from __future__ import annotations
import json
from pathlib import Path
from .models import Lab, Obstacle
def parse_lab(data: dict) -> Lab:
"""从字典解析实验室平面图。"""
obstacles = []
for obs in data.get("obstacles", []):
obstacles.append(
Obstacle(
x=float(obs["x"]),
y=float(obs["y"]),
width=float(obs["width"]),
depth=float(obs["depth"]),
)
)
return Lab(
width=float(data["width"]),
depth=float(data["depth"]),
obstacles=obstacles,
)
def load_lab_from_file(path: str | Path) -> Lab:
"""从 JSON 文件加载实验室平面图。"""
with open(path) as f:
data = json.load(f)
return parse_lab(data)
def create_simple_lab(width: float, depth: float) -> Lab:
"""创建一个无障碍物的简单矩形实验室。"""
return Lab(width=width, depth=depth)

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@@ -1,187 +0,0 @@
# Demo Agent — Lab Layout Orchestrator
You are a lab layout agent for a recorded demo. Your job is to take a natural language lab request, translate it into optimizer constraints, run the optimization, and push results to the 3D frontend — all while outputting only concise, readable status lines.
## CRITICAL OUTPUT RULES
- Output ONLY short status lines. No markdown fences. No raw JSON. No explanations.
- Every HTTP call uses `curl -s` (silent). Never show curl output to the user.
- Parse responses internally. Extract only the fields needed for your status lines.
- Server base URL: `http://localhost:8000`
## Pipeline
Execute these steps in order. Print the status line shown after each step.
### Step 1 — Retrieve devices
Run:
```
curl -s http://localhost:8000/devices
```
Filter to `is_standalone: true` entries. Count them. Build an id→name lookup.
Print:
```
retrieving devices... N standalone devices found
```
Then print an id mapping table showing the user-friendly name → device_id for devices relevant to the user's request:
```
id mapping:
plate hotel → thermo_orbitor_rs2_hotel
robot arm → arm_slider
liquid handler → opentrons_liquid_handler
plate sealer → agilent_plateloc
pcr machine → inheco_odtc_96xl
```
Only include devices that are relevant to the user's request, not the full catalog.
### Step 2 — Translate intent to constraints
Using the rules in `layout_intent_translator.md` (which you have already read), translate the user's natural language request into an intents JSON structure.
Do NOT print the JSON. Instead, print a human-readable constraint summary:
```
translating intent to constraints...
constraints:
hard: arm_slider must reach 4 devices
hard: min spacing 0.05m between all devices
soft: workflow order hotel → liquid handler → sealer → pcr
soft: all devices close together (high priority)
soft: align to cardinal directions
```
### Step 3 — Interpret intents
Send the intents JSON to the interpret endpoint:
```
curl -s -X POST http://localhost:8000/interpret \
-H "Content-Type: application/json" \
-d '{ "intents": [...] }'
```
Capture the `constraints` and `workflow_edges` arrays from the response. Do NOT print anything for this step — it's a silent validation.
If `errors` is non-empty, print:
```
warning: N intents failed to translate
```
### Step 3.5 — Read lab dimensions
```
curl -s http://localhost:8000/scene/lab
```
Returns `{"width": W, "depth": D}`. Use these values for the optimize request. Do NOT print anything for this step.
### Step 4 — Optimize layout
Build the optimize request using:
- `devices`: the relevant devices from Step 1 (id, name, device_type)
- `lab`: the `{"width": W, "depth": D}` from Step 3.5
- `constraints`: from Step 3 interpret response
- `workflow_edges`: from Step 3 interpret response
- `seeder`: `"compact_outward"` (default)
- `seeder_overrides`: generally not needed. Cardinal alignment is handled by the `align_cardinal` intent (generates `prefer_aligned` constraint). Do NOT use `align_weight` in seeder_overrides — it is deprecated.
- `snap_cardinal`: `false` (default). Set `true` only if user explicitly requests snapping to 0/90/180/270.
- `run_de`: `true`
- `maxiter`: `200`
- `seed`: `42`
Run:
```
curl -s -X POST http://localhost:8000/optimize \
-H "Content-Type: application/json" \
-d '{ ... }'
```
Print:
```
optimizing layout (DE, 200 iterations)...
optimization complete — cost: X.XX, success: true/false
```
If `success` is false, print:
```
error: optimization failed (cost: inf) — constraints may conflict
```
And stop.
### Step 5 — Apply placements
Take the `placements` array from the optimize response and POST them. Do NOT add a `location` field — the backend schema only accepts `device_id`, `uuid`, `position`, and `rotation`. Extra fields will cause validation errors.
```
curl -s -X POST http://localhost:8000/scene/placements \
-H "Content-Type: application/json" \
-d '{ "placements": [
{
"device_id": "...",
"uuid": "...",
"position": {"x": ..., "y": ..., "z": ...},
"rotation": {"x": ..., "y": ..., "z": ...}
}
] }'
```
**Important — version-based polling:** The frontend polls `GET /scene/placements` every 1 second and uses a version number to detect changes. On the **first poll**, it captures the current version as a baseline and does **not** apply placements. It only renders placements when the version **increases beyond** that baseline. This means if you POST placements before the frontend has polled once, the frontend will silently skip that update.
**Solution:** After the initial POST, send the **same request a second time** to bump the version. This guarantees the frontend sees a version increase after its baseline poll and applies the placements.
Print:
```
applying placements to scene...
layout applied — N devices positioned
```
## Follow-up Requests
If the user gives a follow-up request (e.g., "now move the sealer farther from the thermal cycler"):
1. Print a `---` separator
2. Keep the same device list (no need to re-fetch)
3. Translate the NEW request into intents — these REPLACE the previous constraints entirely
4. Run Steps 35 again with the new constraints
5. Same output format
## Error Handling
- Server unreachable: `error: server unreachable at localhost:8000`
- Optimize fails: `error: optimization failed (cost: inf) — constraints may conflict`
- After any error, stop and wait for user input.
## Device Name Resolution
You have `layout_intent_translator.md` loaded as context. Use its device name resolution rules to match user's informal names (e.g., "PCR machine", "the arm", "liquid handler") to exact device IDs from the catalog retrieved in Step 1.
## Example Full Output
For input: "Set up a PCR workflow — hotel, liquid handler, sealer, thermal cycler. The arm handles all transfers. Keep it compact."
```
retrieving devices... 47 standalone devices found
id mapping:
plate hotel → thermo_orbitor_rs2_hotel
robot arm → arm_slider
liquid handler → opentrons_liquid_handler
plate sealer → agilent_plateloc
pcr machine → inheco_odtc_96xl
translating intent to constraints...
constraints:
hard: arm_slider must reach 4 devices
soft: workflow order hotel → liquid handler → sealer → pcr
soft: all devices close together (high priority)
soft: align to cardinal directions
optimizing layout (DE, 200 iterations)...
optimization complete — cost: 0.00, success: true
applying placements to scene...
layout applied — 5 devices positioned
```

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@@ -1,312 +0,0 @@
# Layout Intent Translator — LLM Skill
You are a lab layout intent translator. Your job is to convert natural language descriptions of lab layout requirements into structured JSON intents that the layout optimizer can understand.
## Your Role
Users describe their lab needs in natural language. You must:
1. Identify devices by their IDs from the provided device list
2. Infer spatial relationships, workflow order, and physical constraints
3. Output structured intents (JSON) that map to the optimizer's intent schema
4. Provide clear `description` fields so users can verify the translation
## Output Format
You MUST output a JSON object with an `intents` array. Each intent has:
```json
{
"intents": [
{
"intent": "<intent_type>",
"params": { ... },
"description": "Human-readable explanation of what this intent means"
}
]
}
```
## Available Intent Types
### `reachable_by` — Robot arm must reach devices
```json
{
"intent": "reachable_by",
"params": {
"arm": "arm_device_id",
"targets": ["device_a", "device_b"]
},
"description": "Robot arm must be able to reach device A and device B"
}
```
**When to use:** Any time a robot arm transfers items between devices, all those devices must be reachable.
### `close_together` — Devices should be near each other
```json
{
"intent": "close_together",
"params": {
"devices": ["device_a", "device_b", "device_c"],
"priority": "high"
},
"description": "These devices are used frequently together and should be close"
}
```
**Priority:** `"low"` (nice-to-have), `"medium"` (default), `"high"` (critical for workflow speed)
Priority is only part of the intent input. The interpreter automatically bakes it into the emitted constraint `weight`; there is no separate constraint-level `priority` field in `/interpret` output or `/optimize` input.
### `far_apart` — Devices should be separated
```json
{
"intent": "far_apart",
"params": {
"devices": ["heat_source", "reagent_storage"],
"priority": "medium"
}
}
```
**When to use:** Thermal interference, contamination risk, safety separation.
### `keep_adjacent` — Devices should stay adjacent
```json
{
"intent": "keep_adjacent",
"params": {
"devices": ["device_a", "device_b"],
"priority": "high"
}
}
```
**When to use:** User explicitly asks for a pair or group to stay side-by-side / adjacent. This currently maps to the same optimizer behavior as `close_together`, but is semantically more precise.
### `max_distance` — Hard limit on maximum distance
```json
{
"intent": "max_distance",
"params": {
"device_a": "device_a_id",
"device_b": "device_b_id",
"distance": 1.5
}
}
```
**When to use:** Physical constraints like tube length, cable reach, arm range.
### `min_distance` — Hard limit on minimum distance
```json
{
"intent": "min_distance",
"params": {
"device_a": "device_a_id",
"device_b": "device_b_id",
"distance": 0.5
}
}
```
**When to use:** Safety clearance, thermal isolation, vibration separation.
### `min_spacing` — Global minimum gap between all devices
```json
{
"intent": "min_spacing",
"params": { "min_gap": 0.3 }
}
```
**When to use:** General accessibility, maintenance clearance.
### `workflow_hint` — Workflow step ordering
```json
{
"intent": "workflow_hint",
"params": {
"workflow": "pcr",
"devices": ["liquid_handler", "thermal_cycler", "plate_sealer", "storage"]
}
}
```
**When to use:** When user describes a sequential process. Devices are listed in workflow order. Consecutive devices will be placed near each other.
### `face_outward` / `face_inward` / `align_cardinal`
```json
{"intent": "face_outward"}
{"intent": "face_inward"}
{"intent": "align_cardinal"}
```
**When to use:** User mentions accessibility from outside, central robot, or neat alignment.
## Device Name Resolution
You will receive the current scene's device list as context. This is the **only** source of valid device IDs. Users will refer to devices using informal names — you must match them to exact IDs from this list.
### Input Context Format
Before each translation request, you receive the scene's device list:
```
Devices in scene:
- thermo_orbitor_rs2_hotel: Thermo Orbitor RS2 Hotel (type: static, bbox: 0.68×0.52m)
- arm_slider: Arm Slider (type: articulation, bbox: 1.20×0.30m)
- opentrons_liquid_handler: Opentrons Liquid Handler (type: static, bbox: 0.65×0.60m)
- agilent_plateloc: Agilent PlateLoc (type: static, bbox: 0.35×0.40m)
- inheco_odtc_96xl: Inheco ODTC 96XL (type: static, bbox: 0.30×0.35m)
```
### Matching Rules
1. **Exact match first**: If user says "arm_slider", match directly
2. **Name/brand match**: "opentrons" → `opentrons_liquid_handler`, "plateloc" → `agilent_plateloc`
3. **Function match**: "PCR machine" / "thermal cycler" → `inheco_odtc_96xl`; "liquid handler" / "pipetting robot" → `opentrons_liquid_handler`; "plate hotel" / "storage" → `thermo_orbitor_rs2_hotel`; "plate sealer" → `agilent_plateloc`
4. **Type match**: "robot arm" / "the arm" → look for `device_type: articulation`
5. **Ambiguous**: If multiple devices could match, list candidates in the `description` field and pick the most likely one. If truly ambiguous, return an error intent asking the user to clarify.
### Duplicate Device Convention
When the same catalog device appears multiple times in the scene:
- first instance keeps the bare catalog ID, e.g. `plate_reader`
- second and later instances use `#N`, e.g. `plate_reader#2`, `plate_reader#3`
- a bare ID in an intent fans out to all instances
- a suffixed ID applies only to that specific instance
Examples:
- `{"devices": ["plate_reader", "storage_hotel"]}` applies to every `plate_reader` instance
- `{"devices": ["plate_reader#2", "storage_hotel"]}` applies only to the second instance
### Example Resolution
User says: "the robot should reach the PCR machine and the liquid handler"
Scene devices: `arm_slider` (articulation), `inheco_odtc_96xl`, `opentrons_liquid_handler`, ...
Resolution:
- "the robot" → `arm_slider` (only articulation-type device)
- "PCR machine" → `inheco_odtc_96xl` (thermal cycler = PCR)
- "liquid handler" → `opentrons_liquid_handler`
## Translation Rules
### 1. Robot Arm Inference
If any robot arm is in the device list and the workflow involves plate/sample transfer between devices, ALL devices that exchange plates/samples with each other via the arm must be in `reachable_by.targets`.
### 2. Workflow Order
When a user describes a process (e.g., "prepare samples, then run PCR, then seal"), extract the device order and create a `workflow_hint`. The device order follows the sample processing path.
### 3. Implicit Constraints
- If devices frequently exchange items → `close_together` (high priority)
- If user explicitly says "keep these adjacent", "side by side", or "next to each other" → `keep_adjacent`
- If a robot arm is mentioned "in between" → `reachable_by` for all involved devices
- If user says "short transit" or "fast transfer" → `close_together` with `"priority": "high"`
- If user says "keep X away from Y" → `far_apart` or `min_distance`
### 4. Don't Over-Constrain
- Only add constraints the user's description implies
- When unsure about priority, use `"medium"`
- For workflow_hint, confidence is inherently `"low"` — the optimizer notes this
## Example: PCR Workflow
**User input:**
> "Take plate from hotel, prepare sample in opentrons, seal plate then pcr cycle, arm_slider handles all transfers"
**Device list provided:**
- `thermo_orbitor_rs2_hotel` (plate hotel/storage)
- `arm_slider` (robot arm on linear rail)
- `opentrons_liquid_handler` (liquid handling/pipetting)
- `agilent_plateloc` (plate sealer)
- `inheco_odtc_96xl` (thermal cycler for PCR)
**Your output:**
```json
{
"intents": [
{
"intent": "reachable_by",
"params": {
"arm": "arm_slider",
"targets": [
"thermo_orbitor_rs2_hotel",
"opentrons_liquid_handler",
"agilent_plateloc",
"inheco_odtc_96xl"
]
},
"description": "arm_slider must reach all devices since it handles all plate transfers"
},
{
"intent": "workflow_hint",
"params": {
"workflow": "pcr",
"devices": [
"thermo_orbitor_rs2_hotel",
"opentrons_liquid_handler",
"agilent_plateloc",
"inheco_odtc_96xl"
]
},
"description": "PCR workflow order: hotel → liquid handler → plate sealer → thermal cycler"
},
{
"intent": "close_together",
"params": {
"devices": ["opentrons_liquid_handler", "agilent_plateloc"],
"priority": "high"
},
"description": "Sealing happens immediately after sample prep — minimize transit time"
}
]
}
```
**Reasoning:**
- The arm handles ALL transfers → all 4 devices in reachable_by targets
- User described a clear sequence → workflow_hint in that order
- "seal plate then pcr" implies sealing is immediately after prep → close_together for the pair with high priority
## Example: Simple Proximity Request
**User input:**
> "Keep the thermal cycler close to the plate sealer, at most 1 meter apart"
**Your output:**
```json
{
"intents": [
{
"intent": "max_distance",
"params": {
"device_a": "inheco_odtc_96xl",
"device_b": "agilent_plateloc",
"distance": 1.0
},
"description": "Thermal cycler and plate sealer must be within 1 meter"
}
]
}
```
## API Integration
### Discovery
Call `GET /interpret/schema` to get the current list of available intent types and their parameter specifications. Always check this before translating, as new intent types may be added.
### Translation
Send your output to `POST /interpret`:
```
POST /interpret
Content-Type: application/json
{
"intents": [ ... your translated intents ... ]
}
```
### Response
The endpoint returns:
- `constraints` — ready to pass to `/optimize`
- `translations` — human-readable mapping of each intent to generated constraints
- `workflow_edges` — extracted workflow connections
- `errors` — any intents that failed to translate
### Optimization
After user confirms the translation, pass `constraints` and `workflow_edges` to `POST /optimize` along with the device list and lab dimensions.

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@@ -1,110 +0,0 @@
"""Mock 检测器:无 ROS 依赖的简化碰撞与可达性检测。
碰撞检测基于 OBB SATO(n²) 两两比较)。
可达性检测基于最大臂展半径的欧几里得距离判断。
集成阶段由 ros_checkers.py 中的 MoveItCollisionChecker / IKFastReachabilityChecker 替代。
"""
from __future__ import annotations
import math
from .obb import obb_corners, obb_overlap
class MockCollisionChecker:
"""基于 OBB SAT 的碰撞检测。
输入格式与 CollisionChecker Protocol 一致:
placements: [{"id": str, "bbox": (w, d), "pos": (x, y, θ)}, ...]
"""
def check(self, placements: list[dict]) -> list[tuple[str, str]]:
"""返回所有碰撞的设备对。"""
collisions: list[tuple[str, str]] = []
n = len(placements)
for i in range(n):
for j in range(i + 1, n):
a, b = placements[i], placements[j]
corners_a = obb_corners(
a["pos"][0], a["pos"][1],
a["bbox"][0], a["bbox"][1],
a["pos"][2] if len(a["pos"]) > 2 else 0.0,
)
corners_b = obb_corners(
b["pos"][0], b["pos"][1],
b["bbox"][0], b["bbox"][1],
b["pos"][2] if len(b["pos"]) > 2 else 0.0,
)
if obb_overlap(corners_a, corners_b):
collisions.append((a["id"], b["id"]))
return collisions
def check_bounds(
self, placements: list[dict], lab_width: float, lab_depth: float
) -> list[str]:
"""返回超出实验室边界的设备 ID 列表。"""
out_of_bounds: list[str] = []
for p in placements:
hw, hd = self._rotated_half_extents(p)
x, y = p["pos"][:2]
if x - hw < 0 or x + hw > lab_width or y - hd < 0 or y + hd > lab_depth:
out_of_bounds.append(p["id"])
return out_of_bounds
@staticmethod
def _rotated_half_extents(p: dict) -> tuple[float, float]:
"""计算旋转后 AABB 的半宽和半深。"""
w, d = p["bbox"]
theta = p["pos"][2] if len(p["pos"]) > 2 else 0.0
cos_t = abs(math.cos(theta))
sin_t = abs(math.sin(theta))
half_w = (w * cos_t + d * sin_t) / 2
half_d = (w * sin_t + d * cos_t) / 2
return half_w, half_d
class MockReachabilityChecker:
"""基于最大臂展半径的简化可达性判断。
内置常见 Elite CS 系列机械臂的臂展参数。
自定义臂展可通过构造参数传入。
"""
# 默认臂展参数(单位:米)
DEFAULT_ARM_REACH: dict[str, float] = {
"elite_cs63": 0.624,
"elite_cs66": 0.914,
"elite_cs612": 1.304,
"elite_cs620": 1.800,
"arm_slider": 0.3, # 线性导轨臂1.07 body 2.14m × 0.35mreach ≈ half length
}
# 未知型号回退臂展realistic default for lab-scale arms
DEFAULT_FALLBACK_REACH: float = 0.4
def __init__(self, arm_reach: dict[str, float] | None = None):
self.arm_reach = {**self.DEFAULT_ARM_REACH, **(arm_reach or {})}
def is_reachable(self, arm_id: str, arm_pose: dict, target: dict) -> bool:
"""判断目标点是否在机械臂最大臂展半径内。
Uses OBB edge-to-edge distance when available (passed as _obb_dist),
otherwise falls back to center-to-center Euclidean distance.
Args:
arm_id: 机械臂型号 ID用于查臂展
arm_pose: {"x": float, "y": float, "theta": float}
target: {"x": float, "y": float, "z": float, "_obb_dist": float (optional)}
Returns:
True 如果目标在臂展半径内
"""
max_reach = self.arm_reach.get(arm_id, self.DEFAULT_FALLBACK_REACH)
if "_obb_dist" in target:
return target["_obb_dist"] <= max_reach
dx = target["x"] - arm_pose["x"]
dy = target["y"] - arm_pose["y"]
dist_sq = dx**2 + dy**2
return dist_sq <= max_reach**2

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@@ -1,96 +0,0 @@
"""数据模型定义Device, Lab, Placement, Constraint 及 API 请求/响应。"""
from __future__ import annotations
import math
from dataclasses import dataclass, field
from typing import Literal
@dataclass
class Opening:
"""设备的访问开口(用于方向约束)。"""
# 设备局部坐标系中的方向单位向量,如 (0, -1) = 正前方
direction: tuple[float, float] = (0.0, -1.0)
label: str = ""
@dataclass
class Device:
"""设备描述。"""
id: str
name: str
# 碰撞包围盒 (width along X, depth along Y),单位:米
bbox: tuple[float, float] = (0.6, 0.4)
device_type: Literal["static", "articulation", "rigid"] = "static"
# 以下为可选扩展字段(向后兼容)
height: float = 0.4
origin_offset: tuple[float, float] = (0.0, 0.0)
openings: list[Opening] = field(default_factory=list)
source: Literal["registry", "assets", "manual"] = "manual"
model_path: str = ""
model_type: str = ""
thumbnail_url: str = ""
uuid: str = ""
@dataclass
class Obstacle:
"""实验室内固定障碍物(矩形)。"""
x: float
y: float
width: float
depth: float
@dataclass
class Lab:
"""实验室平面图。"""
width: float # X 方向,单位:米
depth: float # Y 方向,单位:米
obstacles: list[Obstacle] = field(default_factory=list)
@dataclass
class Placement:
"""单个设备的布局位姿。"""
device_id: str
x: float
y: float
theta: float # 旋转角,弧度
uuid: str = "" # 前端分配的唯一标识,透传不生成
def rotated_bbox(self, device: Device) -> tuple[float, float]:
"""返回旋转后的 AABB 尺寸 (half_w, half_h)。"""
w, d = device.bbox
cos_t = abs(math.cos(self.theta))
sin_t = abs(math.sin(self.theta))
half_w = (w * cos_t + d * sin_t) / 2
half_h = (w * sin_t + d * cos_t) / 2
return half_w, half_h
@dataclass
class Constraint:
"""约束规则。"""
type: Literal["hard", "soft"]
rule_name: str
# 规则参数,含义因 rule_name 而异
params: dict = field(default_factory=dict)
# 仅 soft 约束使用
weight: float = 1.0
@dataclass
class Intent:
"""LLM 可生成的语义化意图,由 interpreter 翻译为 Constraint 列表。"""
intent: str # 意图类型,如 "reachable_by", "close_together"
params: dict = field(default_factory=dict)
description: str = "" # 可选的自然语言描述(用于审计/调试)

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@@ -1,257 +0,0 @@
"""OBB (Oriented Bounding Box) geometry: corners, overlap (SAT), minimum distance."""
from __future__ import annotations
import math
def obb_corners(
cx: float, cy: float, w: float, h: float, theta: float
) -> list[tuple[float, float]]:
"""Return 4 corners of the OBB as (x, y) tuples.
Args:
cx, cy: center position
w, h: full width and height (not half-extents)
theta: rotation angle in radians
"""
hw, hh = w / 2, h / 2
cos_t, sin_t = math.cos(theta), math.sin(theta)
dx_w, dy_w = hw * cos_t, hw * sin_t # half-width vector
dx_h, dy_h = -hh * sin_t, hh * cos_t # half-height vector
return [
(cx + dx_w + dx_h, cy + dy_w + dy_h),
(cx - dx_w + dx_h, cy - dy_w + dy_h),
(cx - dx_w - dx_h, cy - dy_w - dy_h),
(cx + dx_w - dx_h, cy + dy_w - dy_h),
]
def _get_axes(corners: list[tuple[float, float]]) -> list[tuple[float, float]]:
"""Return 2 edge-normal axes for a rectangle (4 corners)."""
axes = []
for i in range(2): # Only need 2 axes for a rectangle
edge_x = corners[i + 1][0] - corners[i][0]
edge_y = corners[i + 1][1] - corners[i][1]
length = math.sqrt(edge_x**2 + edge_y**2)
if length > 0:
axes.append((-edge_y / length, edge_x / length))
return axes
def _project(corners: list[tuple[float, float]], axis: tuple[float, float]) -> tuple[float, float]:
"""Project all corners onto axis, return (min, max) scalar projections."""
dots = [c[0] * axis[0] + c[1] * axis[1] for c in corners]
return min(dots), max(dots)
def obb_overlap(corners_a: list[tuple[float, float]], corners_b: list[tuple[float, float]]) -> bool:
"""Return True if two OBBs overlap using Separating Axis Theorem.
Uses strict inequality (touching edges = no overlap).
"""
for axis in _get_axes(corners_a) + _get_axes(corners_b):
min_a, max_a = _project(corners_a, axis)
min_b, max_b = _project(corners_b, axis)
if max_a <= min_b or max_b <= min_a:
return False
return True
def _point_to_segment_dist_sq(
px: float, py: float,
ax: float, ay: float,
bx: float, by: float,
) -> float:
"""Squared distance from point (px,py) to line segment (ax,ay)-(bx,by)."""
dx, dy = bx - ax, by - ay
len_sq = dx * dx + dy * dy
if len_sq == 0:
return (px - ax) ** 2 + (py - ay) ** 2
t = max(0.0, min(1.0, ((px - ax) * dx + (py - ay) * dy) / len_sq))
proj_x, proj_y = ax + t * dx, ay + t * dy
return (px - proj_x) ** 2 + (py - proj_y) ** 2
def obb_penetration_depth(
corners_a: list[tuple[float, float]],
corners_b: list[tuple[float, float]],
) -> float:
"""Minimum penetration depth between two OBBs (SAT-based).
Returns 0.0 if not overlapping. Otherwise returns the minimum overlap
along any separating axis — the smallest push needed to separate them.
"""
min_overlap = float("inf")
for axis in _get_axes(corners_a) + _get_axes(corners_b):
min_a, max_a = _project(corners_a, axis)
min_b, max_b = _project(corners_b, axis)
overlap = min(max_a - min_b, max_b - min_a)
if overlap <= 0:
return 0.0 # Separated on this axis
if overlap < min_overlap:
min_overlap = overlap
return min_overlap
def nearest_point_on_obb(
px: float, py: float,
corners: list[tuple[float, float]],
) -> tuple[float, float]:
"""Return the nearest point on an OBB's boundary to point (px, py).
If the point is inside the OBB, returns the nearest edge point.
"""
best_x, best_y = corners[0]
best_dist_sq = float("inf")
n = len(corners)
for i in range(n):
ax, ay = corners[i]
bx, by = corners[(i + 1) % n]
dx, dy = bx - ax, by - ay
len_sq = dx * dx + dy * dy
if len_sq == 0:
proj_x, proj_y = ax, ay
else:
t = max(0.0, min(1.0, ((px - ax) * dx + (py - ay) * dy) / len_sq))
proj_x, proj_y = ax + t * dx, ay + t * dy
d_sq = (px - proj_x) ** 2 + (py - proj_y) ** 2
if d_sq < best_dist_sq:
best_dist_sq = d_sq
best_x, best_y = proj_x, proj_y
return best_x, best_y
def segment_intersects_obb(
p1: tuple[float, float],
p2: tuple[float, float],
corners: list[tuple[float, float]],
) -> bool:
"""Return True if line segment p1-p2 intersects the OBB (convex polygon).
Uses separating axis theorem on the Minkowski difference:
test segment against each edge of the polygon + segment normal.
"""
# Quick: test each edge of the OBB against the segment
n = len(corners)
for i in range(n):
ax, ay = corners[i]
bx, by = corners[(i + 1) % n]
if _segments_intersect(p1[0], p1[1], p2[0], p2[1], ax, ay, bx, by):
return True
# Also check if segment is fully inside the OBB
if _point_in_convex(p1, corners) or _point_in_convex(p2, corners):
return True
return False
def _segments_intersect(
ax: float, ay: float, bx: float, by: float,
cx: float, cy: float, dx: float, dy: float,
) -> bool:
"""Return True if segment AB intersects segment CD (proper or endpoint)."""
def cross(ox: float, oy: float, px: float, py: float, qx: float, qy: float) -> float:
return (px - ox) * (qy - oy) - (py - oy) * (qx - ox)
d1 = cross(cx, cy, dx, dy, ax, ay)
d2 = cross(cx, cy, dx, dy, bx, by)
d3 = cross(ax, ay, bx, by, cx, cy)
d4 = cross(ax, ay, bx, by, dx, dy)
if ((d1 > 0 and d2 < 0) or (d1 < 0 and d2 > 0)) and \
((d3 > 0 and d4 < 0) or (d3 < 0 and d4 > 0)):
return True
# Collinear cases — skip for simplicity (near-zero probability in DE)
return False
def _point_in_convex(
p: tuple[float, float], corners: list[tuple[float, float]]
) -> bool:
"""Return True if point is inside a convex polygon (corners in order)."""
n = len(corners)
sign = None
for i in range(n):
ax, ay = corners[i]
bx, by = corners[(i + 1) % n]
cross = (bx - ax) * (p[1] - ay) - (by - ay) * (p[0] - ax)
if abs(cross) < 1e-12:
continue
s = cross > 0
if sign is None:
sign = s
elif s != sign:
return False
return True
def segment_obb_intersection_length(
p1: tuple[float, float],
p2: tuple[float, float],
corners: list[tuple[float, float]],
) -> float:
"""线段 p1-p2 与 OBB凸多边形的交集长度。
Cyrus-Beck 线段裁剪算法。corners 假定为 CCW 顺序obb_corners 生成)。
无交集返回 0.0。
"""
dx = p2[0] - p1[0]
dy = p2[1] - p1[1]
seg_len_sq = dx * dx + dy * dy
if seg_len_sq < 1e-24:
return 0.0
t_enter = 0.0
t_exit = 1.0
n = len(corners)
for i in range(n):
ax, ay = corners[i]
bx, by = corners[(i + 1) % n]
# CCW 多边形边的外法线: (ey, -ex), e = b - a
ex, ey = bx - ax, by - ay
nx, ny = ey, -ex
denom = nx * dx + ny * dy
numer = nx * (p1[0] - ax) + ny * (p1[1] - ay)
if abs(denom) < 1e-12:
if numer > 0:
return 0.0 # 在此边外侧且平行
continue
t = -numer / denom
if denom < 0:
t_enter = max(t_enter, t) # 进入
else:
t_exit = min(t_exit, t) # 退出
if t_enter > t_exit:
return 0.0
if t_enter >= t_exit:
return 0.0
return (t_exit - t_enter) * math.sqrt(seg_len_sq)
def obb_min_distance(
corners_a: list[tuple[float, float]],
corners_b: list[tuple[float, float]],
) -> float:
"""Minimum distance between two OBBs (convex polygons).
Returns 0.0 if overlapping or touching.
"""
if obb_overlap(corners_a, corners_b):
return 0.0
min_dist_sq = float("inf")
for poly, other in [(corners_a, corners_b), (corners_b, corners_a)]:
n = len(other)
for px, py in poly:
for i in range(n):
ax, ay = other[i]
bx, by = other[(i + 1) % n]
d_sq = _point_to_segment_dist_sq(px, py, ax, ay, bx, by)
if d_sq < min_dist_sq:
min_dist_sq = d_sq
return math.sqrt(min_dist_sq)

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"""初始布局生成Pencil MCP 接口 + 行列式回退。
策略:
1. 尝试调用 Pencil AI MCP 生成初始布局
2. 若 Pencil 不可用或失败,回退到行列式放置算法
行列式回退逻辑:
- 设备按面积从大到小排序
- 沿 X 轴逐个放置,行满(超出 lab.width则换行
- 设备间保留 margin 间距
- 所有设备 θ=0朝向不变
"""
from __future__ import annotations
import logging
from .models import Device, Lab, Placement
logger = logging.getLogger(__name__)
# 设备间最小间距(米)
DEFAULT_MARGIN = 0.3
def generate_initial_layout(
devices: list[Device],
lab: Lab,
margin: float = DEFAULT_MARGIN,
) -> list[Placement]:
"""生成初始布局方案。
优先尝试 Pencil MCP失败则回退到行列式放置。
Args:
devices: 待放置的设备列表
lab: 实验室平面图
margin: 设备间最小间距
Returns:
初始布局 Placement 列表
"""
# 尝试 Pencil MCP
pencil_result = _try_pencil(devices, lab)
if pencil_result is not None:
logger.info("Using Pencil AI generated layout")
return pencil_result
# 回退到行列式
logger.info("Pencil unavailable, using row-based fallback layout")
return generate_fallback(devices, lab, margin)
def _try_pencil(
devices: list[Device],
lab: Lab,
) -> list[Placement] | None:
"""尝试通过 Pencil AI MCP 生成布局。
当前 Pencil MCP 不可用,返回 None 触发回退。
未来集成时,此函数应:
1. 将设备 2D 投影 + 实验室平面图序列化为 Pencil 输入格式
2. 调用 mcp__pencil_* 工具
3. 解析返回的布局方案为 Placement 列表
预留接口参数:
- devices: 设备列表id, bbox
- lab: 实验室尺寸
"""
# TODO: 当 Pencil MCP 可用时实现
# 预期调用方式:
# pencil_input = {
# "floor_plan": {"width": lab.width, "depth": lab.depth},
# "items": [{"id": d.id, "width": d.bbox[0], "depth": d.bbox[1]} for d in devices],
# }
# result = mcp__pencil_layout(pencil_input)
# return [Placement(device_id=r["id"], x=r["x"], y=r["y"], theta=r["theta"]) for r in result]
return None
def generate_fallback(
devices: list[Device],
lab: Lab,
margin: float = DEFAULT_MARGIN,
) -> list[Placement]:
"""行列式回退布局:按面积从大到小排序,逐行放置。
放置规则:
- 设备中心坐标,从左上角开始
- 每行从 margin + half_width 开始
- 行满(下一个设备右边缘超出 lab.width - margin则换行
- 行高取该行最大设备深度
Args:
devices: 待放置的设备列表
lab: 实验室平面图
margin: 设备间最小间距
Returns:
Placement 列表。若实验室空间不足,剩余设备堆叠在右下角并记录警告。
"""
if not devices:
return []
# 按面积从大到小排序
sorted_devices = sorted(devices, key=lambda d: d.bbox[0] * d.bbox[1], reverse=True)
placements: list[Placement] = []
cursor_x = margin
cursor_y = margin
row_height = 0.0
for dev in sorted_devices:
w, d = dev.bbox
half_w = w / 2
half_d = d / 2
# 检查当前行是否放得下
if cursor_x + half_w + margin > lab.width and placements:
# 换行
cursor_x = margin
cursor_y += row_height + margin
row_height = 0.0
# 设备中心位置
cx = cursor_x + half_w
cy = cursor_y + half_d
# 检查是否超出实验室深度
if cy + half_d + margin > lab.depth:
logger.warning(
"Lab space insufficient for device '%s' (%s), "
"placing at overflow position",
dev.id,
dev.bbox,
)
placements.append(Placement(device_id=dev.id, x=cx, y=cy, theta=0.0))
# 更新游标
cursor_x = cx + half_w + margin
row_height = max(row_height, d)
return placements

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@@ -1,29 +0,0 @@
[build-system]
requires = ["setuptools>=68.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "layout-optimizer"
version = "0.1.0"
description = "AI laboratory layout optimizer for Uni-Lab Phase 3"
requires-python = ">=3.10"
dependencies = [
"scipy>=1.10",
"numpy>=1.24",
"fastapi>=0.100",
"uvicorn>=0.20",
"pydantic>=2.0",
]
[project.optional-dependencies]
dev = [
"pytest>=7.0",
"httpx>=0.24",
]
[tool.setuptools.packages.find]
where = ["."]
include = ["layout_optimizer*"]
[tool.pytest.ini_options]
testpaths = ["tests"]

View File

@@ -1,433 +0,0 @@
"""ROS2/MoveIt2 碰撞检测与 IKFast 可达性检测适配器。
集成阶段替换 mock_checkers.py 中的 Mock 实现,
依赖 Uni-Lab-OS 的 moveit2.py 提供的 MoveIt2 Python 接口。
用法:
from .ros_checkers import MoveItCollisionChecker, IKFastReachabilityChecker
# 碰撞检测
checker = MoveItCollisionChecker(moveit2_instance)
collisions = checker.check(placements)
# 可达性检测(体素图 O(1) 查询 + 实时 IK 回退)
reachability = IKFastReachabilityChecker(moveit2_instance, voxel_dir="/path/to/voxels")
reachable = reachability.is_reachable("elite_cs66", arm_pose, target)
环境变量:
LAYOUT_CHECKER_MODE: "mock" | "moveit" — 选择检测器实现(默认 "mock"
LAYOUT_VOXEL_DIR: 预计算体素图目录路径(.npz 文件)
前置条件:
- ROS2 + MoveIt2 运行中
- moveit2.py 中的 MoveIt2 实例已初始化
- 命名规范:碰撞对象使用 {device_id}_ 前缀
"""
from __future__ import annotations
import logging
import math
import os
from pathlib import Path
from typing import TYPE_CHECKING, Any
import numpy as np
from .obb import obb_corners, obb_overlap
if TYPE_CHECKING:
pass
logger = logging.getLogger(__name__)
# ---------- 坐标变换辅助 ----------
def _yaw_to_quat(theta: float) -> tuple[float, float, float, float]:
"""将 2D 旋转角(绕 Z 轴弧度)转换为四元数 (x, y, z, w)。"""
return (0.0, 0.0, math.sin(theta / 2), math.cos(theta / 2))
def _transform_to_arm_frame(
arm_pose: dict, target: dict,
) -> tuple[float, float, float]:
"""将目标点从世界坐标系变换到机械臂基坐标系。
Args:
arm_pose: {"x": float, "y": float, "theta": float}
target: {"x": float, "y": float, "z": float}
Returns:
(local_x, local_y, local_z) 在臂基坐标系中的位置
"""
dx = target["x"] - arm_pose["x"]
dy = target["y"] - arm_pose["y"]
theta = arm_pose.get("theta", 0.0)
cos_t = math.cos(-theta)
sin_t = math.sin(-theta)
local_x = dx * cos_t - dy * sin_t
local_y = dx * sin_t + dy * cos_t
local_z = target.get("z", 0.0)
return (local_x, local_y, local_z)
# ---------- MoveItCollisionChecker ----------
class MoveItCollisionChecker:
"""通过 MoveIt2 PlanningScene 进行碰撞检测。
工作流程:
1. 将所有设备同步为 MoveIt2 碰撞盒({device_id}_ 前缀)
2. 使用 python-fcl 进行精确两两碰撞检测(若可用)
3. 若 FCL 不可用,回退到 OBB SAT 检测
同步到 MoveIt2 确保机器人运动规划也能感知设备布局。
"""
def __init__(
self,
moveit2: Any,
*,
default_height: float = 0.4,
sync_to_scene: bool = True,
):
"""
Args:
moveit2: Uni-Lab-OS moveit2.py 中的 MoveIt2 实例
default_height: 碰撞盒默认高度(米)
sync_to_scene: 是否同步碰撞对象到 MoveIt2 规划场景
"""
self._moveit2 = moveit2
self._default_height = default_height
self._sync_to_scene = sync_to_scene
self._fcl_available = self._check_fcl()
@staticmethod
def _check_fcl() -> bool:
"""检查 python-fcl 是否可用。"""
try:
import fcl # noqa: F401
return True
except ImportError:
return False
def check(self, placements: list[dict]) -> list[tuple[str, str]]:
"""返回碰撞设备对列表。
Args:
placements: [{"id": str, "bbox": (w, d), "pos": (x, y, θ)}, ...]
Returns:
[("device_a", "device_b"), ...] 存在碰撞的设备对
"""
# 同步到 MoveIt2 规划场景
if self._sync_to_scene:
self._sync_collision_objects(placements)
# 碰撞检测
if self._fcl_available:
return self._check_with_fcl(placements)
return self._check_with_obb(placements)
def check_bounds(
self, placements: list[dict], lab_width: float, lab_depth: float,
) -> list[str]:
"""返回超出实验室边界的设备 ID 列表。"""
out_of_bounds: list[str] = []
for p in placements:
hw, hd = self._rotated_half_extents(p)
x, y = p["pos"][:2]
if x - hw < 0 or x + hw > lab_width or y - hd < 0 or y + hd > lab_depth:
out_of_bounds.append(p["id"])
return out_of_bounds
def _sync_collision_objects(self, placements: list[dict]) -> None:
"""将设备布局同步到 MoveIt2 规划场景。
使用 {device_id}_ 前缀命名碰撞对象。
"""
for p in placements:
obj_id = f"{p['id']}_"
w, d = p["bbox"]
x, y = p["pos"][:2]
theta = p["pos"][2] if len(p["pos"]) > 2 else 0.0
h = self._default_height
try:
self._moveit2.add_collision_box(
id=obj_id,
size=(w, d, h),
position=(x, y, h / 2),
quat_xyzw=_yaw_to_quat(theta),
)
except Exception:
logger.warning("Failed to sync collision object %s", obj_id, exc_info=True)
def _check_with_fcl(self, placements: list[dict]) -> list[tuple[str, str]]:
"""使用 python-fcl 进行精确碰撞检测。"""
import fcl
objects: list[tuple[str, Any]] = []
for p in placements:
w, d = p["bbox"]
h = self._default_height
x, y = p["pos"][:2]
theta = p["pos"][2] if len(p["pos"]) > 2 else 0.0
geom = fcl.Box(w, d, h)
tf = fcl.Transform(
_yaw_to_rotation_matrix(theta),
np.array([x, y, h / 2]),
)
obj = fcl.CollisionObject(geom, tf)
objects.append((p["id"], obj))
collisions: list[tuple[str, str]] = []
n = len(objects)
for i in range(n):
for j in range(i + 1, n):
id_a, obj_a = objects[i]
id_b, obj_b = objects[j]
request = fcl.CollisionRequest()
result = fcl.CollisionResult()
ret = fcl.collide(obj_a, obj_b, request, result)
if ret > 0:
collisions.append((id_a, id_b))
return collisions
def _check_with_obb(self, placements: list[dict]) -> list[tuple[str, str]]:
"""OBB SAT 回退检测(与 MockCollisionChecker 相同算法)。"""
collisions: list[tuple[str, str]] = []
n = len(placements)
for i in range(n):
for j in range(i + 1, n):
a, b = placements[i], placements[j]
corners_a = obb_corners(
a["pos"][0], a["pos"][1],
a["bbox"][0], a["bbox"][1],
a["pos"][2] if len(a["pos"]) > 2 else 0.0,
)
corners_b = obb_corners(
b["pos"][0], b["pos"][1],
b["bbox"][0], b["bbox"][1],
b["pos"][2] if len(b["pos"]) > 2 else 0.0,
)
if obb_overlap(corners_a, corners_b):
collisions.append((a["id"], b["id"]))
return collisions
@staticmethod
def _rotated_half_extents(p: dict) -> tuple[float, float]:
"""计算旋转后 AABB 的半宽和半深。"""
w, d = p["bbox"]
theta = p["pos"][2] if len(p["pos"]) > 2 else 0.0
cos_t = abs(math.cos(theta))
sin_t = abs(math.sin(theta))
half_w = (w * cos_t + d * sin_t) / 2
half_d = (w * sin_t + d * cos_t) / 2
return half_w, half_d
# ---------- IKFastReachabilityChecker ----------
class IKFastReachabilityChecker:
"""基于 MoveIt2 compute_ik 和预计算体素图的可达性检测。
双模式:
1. 体素图模式O(1)):从 .npz 文件加载预计算可达性网格,
将目标点变换到臂基坐标系后直接查表。
2. 实时 IK 模式(~5ms/call调用 MoveIt2.compute_ik()
支持约束感知的精确可达性判断。
优先使用体素图,无匹配时回退到实时 IK。
"""
def __init__(
self,
moveit2: Any = None,
*,
voxel_dir: str | Path | None = None,
voxel_resolution: float = 0.01,
):
"""
Args:
moveit2: MoveIt2 实例(用于实时 IK 回退)
voxel_dir: 预计算体素图目录(.npz 文件,文件名 = arm_id
voxel_resolution: 体素分辨率(米),用于坐标 → 索引转换
"""
self._moveit2 = moveit2
self._voxel_resolution = voxel_resolution
self._voxel_maps: dict[str, _VoxelMap] = {}
if voxel_dir is not None:
self._load_voxel_maps(Path(voxel_dir))
def is_reachable(self, arm_id: str, arm_pose: dict, target: dict) -> bool:
"""判断机械臂在给定位姿下能否到达目标点。
Args:
arm_id: 机械臂设备 ID
arm_pose: {"x": float, "y": float, "theta": float}
target: {"x": float, "y": float, "z": float}
Returns:
True 如果可达
"""
local = _transform_to_arm_frame(arm_pose, target)
# 1. 体素图查询O(1)
if arm_id in self._voxel_maps:
return self._check_voxel(arm_id, local)
# 2. 实时 IK 回退
if self._moveit2 is not None:
return self._check_live_ik(local)
# 无可用检测方式,乐观返回(记录警告)
logger.warning(
"No reachability checker available for arm %s, returning True", arm_id,
)
return True
def _load_voxel_maps(self, voxel_dir: Path) -> None:
"""加载目录下所有 .npz 体素图文件。
文件格式:{arm_id}.npz包含
- "grid": bool ndarray (nx, ny, nz) — True 表示可达
- "origin": float ndarray (3,) — 网格原点(臂基坐标系)
- "resolution": float — 体素分辨率(米)
"""
if not voxel_dir.exists():
logger.warning("Voxel directory does not exist: %s", voxel_dir)
return
for npz_file in voxel_dir.glob("*.npz"):
arm_id = npz_file.stem
try:
data = np.load(str(npz_file))
grid = data["grid"].astype(bool)
origin = data["origin"].astype(float)
resolution = float(data.get("resolution", self._voxel_resolution))
self._voxel_maps[arm_id] = _VoxelMap(
grid=grid, origin=origin, resolution=resolution,
)
logger.info(
"Loaded voxel map for %s: shape=%s, resolution=%.3f",
arm_id, grid.shape, resolution,
)
except Exception:
logger.warning("Failed to load voxel map %s", npz_file, exc_info=True)
def _check_voxel(self, arm_id: str, local: tuple[float, float, float]) -> bool:
"""通过体素网格查询可达性。"""
vm = self._voxel_maps[arm_id]
ix = int(round((local[0] - vm.origin[0]) / vm.resolution))
iy = int(round((local[1] - vm.origin[1]) / vm.resolution))
iz = int(round((local[2] - vm.origin[2]) / vm.resolution))
if (
0 <= ix < vm.grid.shape[0]
and 0 <= iy < vm.grid.shape[1]
and 0 <= iz < vm.grid.shape[2]
):
return bool(vm.grid[ix, iy, iz])
# 超出体素图范围 → 不可达
return False
def _check_live_ik(self, local: tuple[float, float, float]) -> bool:
"""调用 MoveIt2.compute_ik() 进行实时可达性检测。
compute_ik 返回 JointState成功或 None不可达
使用默认朝下姿态(四元数 0, 1, 0, 0 即绕 X 轴旋转 180°
"""
# 目标姿态:末端执行器朝下
quat_xyzw = (0.0, 1.0, 0.0, 0.0)
try:
result = self._moveit2.compute_ik(
position=local,
quat_xyzw=quat_xyzw,
)
return result is not None
except Exception:
logger.warning("compute_ik call failed", exc_info=True)
return False
# ---------- 体素图数据类 ----------
class _VoxelMap:
"""预计算可达性体素网格。"""
__slots__ = ("grid", "origin", "resolution")
def __init__(
self,
grid: np.ndarray,
origin: np.ndarray,
resolution: float,
):
self.grid = grid
self.origin = origin
self.resolution = resolution
# ---------- FCL 辅助 ----------
def _yaw_to_rotation_matrix(theta: float) -> np.ndarray:
"""绕 Z 轴旋转矩阵3×3"""
c, s = math.cos(theta), math.sin(theta)
return np.array([
[c, -s, 0.0],
[s, c, 0.0],
[0.0, 0.0, 1.0],
])
# ---------- 工厂函数 ----------
def create_checkers(
moveit2: Any = None,
*,
mode: str | None = None,
voxel_dir: str | None = None,
) -> tuple[Any, Any]:
"""根据环境变量或参数创建检测器实例。
Args:
moveit2: MoveIt2 实例moveit 模式必需)
mode: "mock" | "moveit"(默认从 LAYOUT_CHECKER_MODE 环境变量读取)
voxel_dir: 体素图目录(默认从 LAYOUT_VOXEL_DIR 环境变量读取)
Returns:
(collision_checker, reachability_checker)
"""
if mode is None:
mode = os.getenv("LAYOUT_CHECKER_MODE", "mock")
if mode == "moveit":
if moveit2 is None:
raise ValueError("MoveIt2 instance required for 'moveit' checker mode")
if voxel_dir is None:
voxel_dir = os.getenv("LAYOUT_VOXEL_DIR")
collision = MoveItCollisionChecker(moveit2)
reachability = IKFastReachabilityChecker(
moveit2, voxel_dir=voxel_dir,
)
logger.info("Using MoveIt2 checkers (voxel_dir=%s)", voxel_dir)
return collision, reachability
# 默认mock 模式
from .mock_checkers import MockCollisionChecker, MockReachabilityChecker
logger.info("Using mock checkers")
return MockCollisionChecker(), MockReachabilityChecker()

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@@ -1,331 +0,0 @@
"""Force-directed seeder engine with named parameter presets.
Produces initial device placements for the layout optimizer.
Different layout strategies (compact, spread, workflow-aware) are
parameter configurations of the same force-directed simulation engine.
"""
from __future__ import annotations
import logging
import math
from dataclasses import dataclass, replace
from .models import Device, Lab, Placement
from .obb import obb_corners, obb_overlap, obb_min_distance
logger = logging.getLogger(__name__)
@dataclass
class SeederParams:
"""Parameters for the force-directed seeder engine."""
boundary_attraction: float = 0.0 # >0 push to walls, <0 push to center
mutual_repulsion: float = 1.0 # inter-device repulsion strength
edge_attraction: float = 0.0 # workflow edge attraction (Stage 2)
orientation_mode: str = "none" # "outward" | "inward" | "none"
PRESETS: dict[str, SeederParams | None] = {
"compact_outward": SeederParams(
boundary_attraction=-1.0, mutual_repulsion=0.5, orientation_mode="outward",
),
"spread_inward": SeederParams(
boundary_attraction=1.0, mutual_repulsion=1.0, orientation_mode="inward",
),
"workflow_cluster": SeederParams(
boundary_attraction=-0.5, mutual_repulsion=0.5,
edge_attraction=1.0, orientation_mode="outward",
),
"row_fallback": None, # Delegates to generate_fallback()
}
def resolve_seeder_params(
preset_name: str, overrides: dict | None = None,
) -> SeederParams | None:
"""Look up preset by name and apply overrides."""
if preset_name not in PRESETS:
raise ValueError(
f"Unknown seeder preset '{preset_name}'. "
f"Available: {list(PRESETS.keys())}"
)
params = PRESETS[preset_name]
if params is None or not overrides:
return params
return replace(params, **{k: v for k, v in overrides.items() if hasattr(params, k)})
def seed_layout(
devices: list[Device],
lab: Lab,
params: SeederParams | None,
edges: list[list[str]] | None = None,
) -> list[Placement]:
"""Generate initial device placements using force-directed simulation.
Args:
devices: devices to place
lab: lab dimensions
params: seeder parameters (None = row_fallback)
edges: workflow edges as [device_a_id, device_b_id] pairs (Stage 2)
Returns:
list of Placement objects, one per device
"""
if not devices:
return []
if params is None:
from .pencil_integration import generate_fallback
return generate_fallback(devices, lab)
return _force_simulation(devices, lab, params, edges)
def _force_simulation(
devices: list[Device],
lab: Lab,
params: SeederParams,
edges: list[list[str]] | None = None,
max_iter: int = 80,
dt: float = 0.05,
damping: float = 0.8,
) -> list[Placement]:
"""Run force-directed simulation to produce initial placements."""
n = len(devices)
center_x, center_y = lab.width / 2, lab.depth / 2
# Initialize positions: grid layout within lab bounds
cols = max(1, int(math.ceil(math.sqrt(n))))
rows_count = max(1, math.ceil(n / cols))
positions = [] # (x, y) per device
for i, dev in enumerate(devices):
row, col = divmod(i, cols)
margin = 0.3
x = margin + (col + 0.5) * (lab.width - 2 * margin) / cols
y = margin + (row + 0.5) * (lab.depth - 2 * margin) / rows_count
x = min(max(x, dev.bbox[0] / 2), lab.width - dev.bbox[0] / 2)
y = min(max(y, dev.bbox[1] / 2), lab.depth - dev.bbox[1] / 2)
positions.append([x, y])
# Initialize orientations
thetas = [0.0] * n
# Build edge lookup for Stage 2
edge_set: set[tuple[int, int]] = set()
if edges and params.edge_attraction > 0:
id_to_idx = {d.id: i for i, d in enumerate(devices)}
for e in edges:
if len(e) == 2 and e[0] in id_to_idx and e[1] in id_to_idx:
edge_set.add((id_to_idx[e[0]], id_to_idx[e[1]]))
converged = False
for iteration in range(max_iter):
forces = [[0.0, 0.0] for _ in range(n)]
total_force = 0.0
# 1. Boundary force
for i in range(n):
dx = positions[i][0] - center_x
dy = positions[i][1] - center_y
dist_to_center = math.sqrt(dx * dx + dy * dy) + 1e-9
f = params.boundary_attraction
forces[i][0] += f * dx / dist_to_center
forces[i][1] += f * dy / dist_to_center
# 2. Mutual repulsion (OBB edge-to-edge)
for i in range(n):
for j in range(i + 1, n):
ci = obb_corners(
positions[i][0], positions[i][1],
devices[i].bbox[0], devices[i].bbox[1], thetas[i],
)
cj = obb_corners(
positions[j][0], positions[j][1],
devices[j].bbox[0], devices[j].bbox[1], thetas[j],
)
dist = obb_min_distance(ci, cj)
if dist < 1e-9:
dist = 0.01 # Prevent division by zero for overlapping
dx = positions[i][0] - positions[j][0]
dy = positions[i][1] - positions[j][1]
d_center = math.sqrt(dx * dx + dy * dy) + 1e-9
repulsion = params.mutual_repulsion / (dist * dist + 0.1)
fx = repulsion * dx / d_center
fy = repulsion * dy / d_center
forces[i][0] += fx
forces[i][1] += fy
forces[j][0] -= fx
forces[j][1] -= fy
# 3. Edge attraction (Stage 2)
if params.edge_attraction > 0:
for i_idx, j_idx in edge_set:
dx = positions[j_idx][0] - positions[i_idx][0]
dy = positions[j_idx][1] - positions[i_idx][1]
dist = math.sqrt(dx * dx + dy * dy) + 1e-9
f = params.edge_attraction * dist * 0.1
forces[i_idx][0] += f * dx / dist
forces[i_idx][1] += f * dy / dist
forces[j_idx][0] -= f * dx / dist
forces[j_idx][1] -= f * dy / dist
# 4. Update positions (Euler + damping)
for i in range(n):
positions[i][0] += forces[i][0] * dt * damping
positions[i][1] += forces[i][1] * dt * damping
total_force += math.sqrt(forces[i][0]**2 + forces[i][1]**2)
# 5. Update orientations
if params.orientation_mode != "none":
for i in range(n):
thetas[i] = _compute_orientation(
positions[i][0], positions[i][1],
center_x, center_y,
devices[i], params.orientation_mode,
)
# 6. Clamp to lab bounds
for i in range(n):
hw, hh = devices[i].bbox[0] / 2, devices[i].bbox[1] / 2
positions[i][0] = max(hw, min(lab.width - hw, positions[i][0]))
positions[i][1] = max(hh, min(lab.depth - hh, positions[i][1]))
if total_force < 0.01 * n:
converged = True
logger.info("Force simulation converged at iteration %d", iteration)
break
if not converged:
logger.info("Force simulation reached max iterations (%d)", max_iter)
placements = [
Placement(device_id=devices[i].id, x=positions[i][0], y=positions[i][1], theta=thetas[i])
for i in range(n)
]
# Log initial collision count
initial_collisions = _count_collisions(devices, placements)
logger.info("Seeder: %d initial collision pairs before resolution", initial_collisions)
# Collision resolution pass
placements = _resolve_collisions(devices, placements, lab, max_passes=5)
# Log diagnostics
final_collisions = _count_collisions(devices, placements)
no_openings = sum(1 for d in devices if not d.openings)
logger.info(
"Seeder complete: %d devices, %d without openings, %d collision pairs remaining",
n, no_openings, final_collisions,
)
return placements
def _compute_orientation(
x: float, y: float,
center_x: float, center_y: float,
device: Device,
mode: str,
) -> float:
"""Compute theta so the device's front faces outward or inward."""
dx = x - center_x
dy = y - center_y
if abs(dx) < 1e-9 and abs(dy) < 1e-9:
return 0.0
angle_to_device = math.atan2(dy, dx)
if device.openings:
front = device.openings[0].direction
else:
front = (0.0, -1.0) # Default: -Y is front
front_angle = math.atan2(front[1], front[0])
if mode == "outward":
target = angle_to_device
elif mode == "inward":
target = angle_to_device + math.pi
else:
return 0.0
return (target - front_angle) % (2 * math.pi)
def _count_collisions(devices: list[Device], placements: list[Placement]) -> int:
"""Count OBB collision pairs (for diagnostics logging)."""
n = len(devices)
count = 0
for i in range(n):
for j in range(i + 1, n):
ci = obb_corners(placements[i].x, placements[i].y,
devices[i].bbox[0], devices[i].bbox[1], placements[i].theta)
cj = obb_corners(placements[j].x, placements[j].y,
devices[j].bbox[0], devices[j].bbox[1], placements[j].theta)
if obb_overlap(ci, cj):
count += 1
return count
def _resolve_collisions(
devices: list[Device],
placements: list[Placement],
lab: Lab,
max_passes: int = 5,
) -> list[Placement]:
"""Push overlapping devices apart. Returns new placement list."""
positions = [[p.x, p.y] for p in placements]
thetas = [p.theta for p in placements]
n = len(devices)
for pass_num in range(max_passes):
has_collision = False
for i in range(n):
for j in range(i + 1, n):
ci = obb_corners(
positions[i][0], positions[i][1],
devices[i].bbox[0], devices[i].bbox[1], thetas[i],
)
cj = obb_corners(
positions[j][0], positions[j][1],
devices[j].bbox[0], devices[j].bbox[1], thetas[j],
)
if obb_overlap(ci, cj):
has_collision = True
dx = positions[i][0] - positions[j][0]
dy = positions[i][1] - positions[j][1]
dist = math.sqrt(dx * dx + dy * dy) + 1e-9
push = 0.5 * (
max(devices[i].bbox[0], devices[i].bbox[1])
+ max(devices[j].bbox[0], devices[j].bbox[1])
) / 4
positions[i][0] += push * dx / dist
positions[i][1] += push * dy / dist
positions[j][0] -= push * dx / dist
positions[j][1] -= push * dy / dist
# Clamp to bounds (rotation-aware AABB half-extents)
for i in range(n):
cos_t = abs(math.cos(thetas[i]))
sin_t = abs(math.sin(thetas[i]))
hw = (devices[i].bbox[0] * cos_t + devices[i].bbox[1] * sin_t) / 2
hh = (devices[i].bbox[0] * sin_t + devices[i].bbox[1] * cos_t) / 2
positions[i][0] = max(hw, min(lab.width - hw, positions[i][0]))
positions[i][1] = max(hh, min(lab.depth - hh, positions[i][1]))
if not has_collision:
logger.info("Collision resolution complete after %d passes", pass_num + 1)
break
else:
logger.warning(
"Collision resolution: %d passes exhausted, collisions may remain",
max_passes,
)
return [
Placement(device_id=placements[i].device_id,
x=positions[i][0], y=positions[i][1],
theta=thetas[i], uuid=placements[i].uuid)
for i in range(n)
]

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@@ -1,742 +0,0 @@
"""FastAPI 开发服务器。
开发阶段独立运行于 localhost:8000前端通过 CORS 调用。
集成阶段合并到 Uni-Lab-OS 的 FastAPI 服务中。
运行方式:
uvicorn unilabos.layout_optimizer.server:app --host 0.0.0.0 --port 8000 --reload
调试模式(启用 DEBUG 日志,含优化器逐代 cost 明细):
LAYOUT_DEBUG=1 uvicorn unilabos.layout_optimizer.server:app --host 0.0.0.0 --port 8000 --reload
日志文件:
自动写入 layout_optimizer/logs/{YYYYMMDD_HHMMSS}.log始终 DEBUG 级别)。
前端 1s 轮询的 GET /scene/placements 200 行不写入日志文件。
前端访问:
http://localhost:8000/
"""
from __future__ import annotations
from collections import defaultdict
import itertools
import logging
import logging.handlers
import math
import os
from datetime import datetime
from pathlib import Path
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse, RedirectResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from .constraints import DEFAULT_WEIGHT_ANGLE # noqa: F401 — kept for external use
from .device_catalog import (
create_devices_from_list,
load_devices_from_assets,
load_devices_from_registry,
load_footprints,
merge_device_lists,
)
from .lab_parser import parse_lab
from .intent_interpreter import InterpretResult, interpret_intents
from .models import Constraint, Intent
from .optimizer import optimize
_console_level = logging.DEBUG if os.getenv("LAYOUT_DEBUG") else logging.INFO
# root logger must be DEBUG so the file handler receives all records;
# console output level is controlled separately via its handler.
logging.basicConfig(level=logging.DEBUG)
# basicConfig creates a default StreamHandler — set its level to the console level
for _h in logging.getLogger().handlers:
if isinstance(_h, logging.StreamHandler):
_h.setLevel(_console_level)
logger = logging.getLogger(__name__)
# --- 文件日志:实时写入 logs/ 目录,按启动时间命名 ---
_LOG_DIR = Path(__file__).parent / "logs"
_LOG_DIR.mkdir(exist_ok=True)
_log_file = _LOG_DIR / f"{datetime.now():%Y%m%d_%H%M%S}.log"
class _PollingFilter(logging.Filter):
"""过滤掉前端 1s 轮询产生的 GET /scene/placements 日志行。"""
def filter(self, record: logging.LogRecord) -> bool:
msg = record.getMessage()
if "GET /scene/placements" in msg and "200" in msg:
return False
return True
_file_handler = logging.FileHandler(_log_file, encoding="utf-8")
_file_handler.setLevel(logging.DEBUG)
_file_handler.setFormatter(
logging.Formatter("%(asctime)s %(levelname)-5s [%(name)s] %(message)s")
)
_file_handler.addFilter(_PollingFilter())
logging.getLogger().addHandler(_file_handler)
STATIC_DIR = Path(__file__).parent / "static"
# 可配置路径
# __file__ -> Uni-Lab-OS/unilabos/layout_optimizer/server.py
_UNILABOS_DIR = Path(__file__).resolve().parent.parent # .../Uni-Lab-OS/unilabos/
UNI_LAB_ASSETS_DIR = Path(
os.getenv("UNI_LAB_ASSETS_DIR", str(_UNILABOS_DIR.parent.parent.parent / "uni-lab-assets"))
)
UNI_LAB_ASSETS_MODELS_DIR = UNI_LAB_ASSETS_DIR / "device_models"
UNI_LAB_ASSETS_DATA_JSON = UNI_LAB_ASSETS_DIR / "data.json"
UNI_LAB_OS_DEVICE_MESH_DIR = Path(
os.getenv(
"UNI_LAB_OS_DEVICE_MESH_DIR",
str(_UNILABOS_DIR / "device_mesh" / "devices"),
)
)
app = FastAPI(title="Layout Optimizer", version="0.2.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # 开发阶段允许所有来源
allow_methods=["*"],
allow_headers=["*"],
)
# 挂载静态文件目录
app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
# 挂载 3D 模型和缩略图
if UNI_LAB_ASSETS_MODELS_DIR.exists():
app.mount("/models", StaticFiles(directory=str(UNI_LAB_ASSETS_MODELS_DIR)), name="models")
logger.info("Mounted /models from %s", UNI_LAB_ASSETS_MODELS_DIR)
else:
logger.warning("uni-lab-assets models dir not found: %s", UNI_LAB_ASSETS_MODELS_DIR)
# ---------- 设备目录缓存 ----------
_device_cache: list[dict] | None = None
_DEVICE_PARAM_KEYS = {"device_a", "device_b", "arm_id", "target_device_id", "device"}
# 消耗品/配件关键词(不独立放置于实验台)
_CONSUMABLE_KEYWORDS = {
"plate", "well", "tube", "tip", "reservoir", "carrier", "nest",
"adapter", "trough", "magnet_module", "magnet_plate", "rack", "lid",
"seal", "cap", "vial", "flask", "dish", "block", "strip", "insert",
"gasket", "pad", "grid_segment", "spacer", "diti_tray",
}
# 但包含这些关键词的是独立设备,不是消耗品
_DEVICE_KEYWORDS = {
"reader", "handler", "hotel", "washer", "stacker", "sealer", "labeler",
"centrifuge", "incubator", "shaker", "robot", "arm", "flex", "dispenser",
"printer", "scanner", "analyzer", "fluorometer", "spectrophotometer",
"thermocycler", "module",
}
def _is_standalone_device(device_id: str, bbox: tuple[float, float]) -> bool:
"""判断设备是否独立放置于实验台(非消耗品/配件)。"""
mx = max(bbox[0], bbox[1])
mn = min(bbox[0], bbox[1])
if mx >= 0.30:
return True # 大于 30cm 一定是独立设备
if mx < 0.05:
return False # 小于 5cm 一定是消耗品
lower = device_id.lower()
# 非常扁平(一维 < 3cm的几乎都是配件/载具,即使名称匹配设备关键词
if mn < 0.03:
return False
# 先检查消耗品关键词(如果匹配,再看是否有设备关键词覆盖)
is_consumable_name = any(kw in lower for kw in _CONSUMABLE_KEYWORDS)
is_device_name = any(kw in lower for kw in _DEVICE_KEYWORDS)
if is_consumable_name and not is_device_name:
return False
if is_device_name:
return True
# 默认:>= 15cm 视为设备
return mx >= 0.15
def _build_device_list() -> list[dict]:
"""构建合并后的设备列表(缓存)。"""
global _device_cache
if _device_cache is not None:
return _device_cache
footprints = load_footprints()
registry = load_devices_from_registry(UNI_LAB_OS_DEVICE_MESH_DIR, footprints)
assets = load_devices_from_assets(UNI_LAB_ASSETS_DATA_JSON, footprints)
merged = merge_device_lists(registry, assets)
_device_cache = [
{
"id": d.id,
"name": d.name,
"device_type": d.device_type,
"source": d.source,
"bbox": list(d.bbox),
"height": d.height,
"origin_offset": list(d.origin_offset),
"openings": [
{"direction": list(o.direction), "label": o.label}
for o in d.openings
],
"model_path": d.model_path,
"model_type": d.model_type,
"thumbnail_url": d.thumbnail_url,
"is_standalone": _is_standalone_device(d.id, d.bbox),
}
for d in merged
]
standalone = sum(1 for d in _device_cache if d["is_standalone"])
logger.info("Built device catalog: %d devices (%d standalone)", len(_device_cache), standalone)
return _device_cache
def _catalog_id_from_internal(device_id: str) -> str:
"""内部实例 ID → catalog ID。"""
return device_id.split("#", 1)[0]
def _expand_constraints_for_duplicates(
constraints: list[Constraint], devices: list,
) -> list[Constraint]:
"""将引用 bare catalog ID 的约束扩展到所有重复实例。"""
catalog_instances: dict[str, list[str]] = defaultdict(list)
for dev in devices:
catalog_instances[_catalog_id_from_internal(dev.id)].append(dev.id)
expanded_constraints: list[Constraint] = []
for constraint in constraints:
fan_out_keys: list[str] = []
fan_out_values: list[list[str]] = []
for key in _DEVICE_PARAM_KEYS:
if key not in constraint.params:
continue
ref_id = constraint.params[key]
if "#" in ref_id:
continue
instances = catalog_instances.get(ref_id, [])
if len(instances) > 1:
fan_out_keys.append(key)
fan_out_values.append(instances)
logger.info(
"Fan-out: %s %s=%s -> %d instances",
constraint.rule_name, key, ref_id, len(instances),
)
if not fan_out_keys:
expanded_constraints.append(constraint)
continue
for combo in itertools.product(*fan_out_values):
new_params = dict(constraint.params)
for key, internal_id in zip(fan_out_keys, combo):
new_params[key] = internal_id
expanded_constraints.append(
Constraint(
type=constraint.type,
rule_name=constraint.rule_name,
params=new_params,
weight=constraint.weight,
)
)
return expanded_constraints
def _maybe_add_prefer_aligned_constraint(
constraints: list[Constraint], align_weight: float,
) -> list[Constraint]:
"""仅在用户未显式提供 prefer_aligned 时注入对齐约束。"""
if align_weight <= 0:
return constraints
if any(c.rule_name == "prefer_aligned" for c in constraints):
logger.info("Skipping auto-injected prefer_aligned because one already exists")
return constraints
constraints.append(
Constraint(
type="soft",
rule_name="prefer_aligned",
weight=align_weight,
)
)
return constraints
# ---------- 路由 ----------
@app.get("/", include_in_schema=False)
async def root():
return RedirectResponse(url="/lab3d")
@app.get("/lab3d", include_in_schema=False)
async def lab3d_ui():
return FileResponse(STATIC_DIR / "lab3d.html")
@app.get("/devices")
async def list_devices(source: str = "all"):
"""返回合并后的设备目录。?source=registry|assets|all"""
devices = _build_device_list()
if source != "all":
devices = [d for d in devices if d["source"] == source]
return devices
@app.get("/health")
async def health():
return {"status": "ok"}
# ---------- 意图解释 API ----------
class IntentSpec(BaseModel):
intent: str
params: dict = {}
description: str = ""
class TranslationEntry(BaseModel):
source_intent: str
source_description: str
source_params: dict
generated_constraints: list[dict]
explanation: str
confidence: str = "high"
class InterpretRequest(BaseModel):
intents: list[IntentSpec]
class InterpretResponse(BaseModel):
constraints: list[dict]
translations: list[TranslationEntry]
workflow_edges: list[list[str]]
errors: list[str]
@app.post("/interpret", response_model=InterpretResponse)
async def run_interpret(request: InterpretRequest):
"""将语义化意图翻译为约束列表,供用户确认后传入 /optimize。"""
logger.info("Interpret request: %d intents", len(request.intents))
intents = [
Intent(
intent=i.intent,
params=i.params,
description=i.description,
)
for i in request.intents
]
result: InterpretResult = interpret_intents(intents)
return InterpretResponse(
constraints=[
{"type": c.type, "rule_name": c.rule_name, "params": c.params, "weight": c.weight}
for c in result.constraints
],
translations=[
TranslationEntry(
source_intent=t["source_intent"],
source_description=t.get("source_description", ""),
source_params=t.get("source_params", {}),
generated_constraints=t["generated_constraints"],
explanation=t["explanation"],
confidence=t.get("confidence", "high"),
)
for t in result.translations
],
workflow_edges=result.workflow_edges,
errors=result.errors,
)
@app.get("/interpret/schema")
async def interpret_schema():
"""返回可用意图类型及其参数规范,供 LLM agent 发现和使用。"""
return {
"description": "Layout optimizer intent schema. LLM agents should translate user requests into these intents.",
"intents": {
"reachable_by": {
"description": "Robot arm must be able to reach all target devices",
"params": {
"arm": {"type": "string", "required": True, "description": "Device ID of robot arm"},
"targets": {"type": "list[string]", "required": True, "description": "Device IDs the arm must reach"},
},
"generates": "hard reachability constraint per target",
},
"close_together": {
"description": "Group of devices should be placed near each other",
"params": {
"devices": {"type": "list[string]", "required": True, "description": "Device IDs (min 2)"},
"priority": {"type": "string", "required": False, "default": "medium", "enum": ["low", "medium", "high"]},
},
"generates": "soft minimize_distance for each pair",
},
"far_apart": {
"description": "Devices should be placed far from each other",
"params": {
"devices": {"type": "list[string]", "required": True, "description": "Device IDs (min 2)"},
"priority": {"type": "string", "required": False, "default": "medium", "enum": ["low", "medium", "high"]},
},
"generates": "soft maximize_distance for each pair",
},
"keep_adjacent": {
"description": "Devices should stay adjacent, similar to close_together",
"params": {
"devices": {"type": "list[string]", "required": True, "description": "Device IDs (min 2)"},
"priority": {"type": "string", "required": False, "default": "medium", "enum": ["low", "medium", "high"]},
},
"generates": "soft minimize_distance for each pair",
},
"max_distance": {
"description": "Two devices must be within a maximum distance",
"params": {
"device_a": {"type": "string", "required": True},
"device_b": {"type": "string", "required": True},
"distance": {"type": "float", "required": True, "description": "Max edge-to-edge distance in meters"},
},
"generates": "hard distance_less_than",
},
"min_distance": {
"description": "Two devices must be at least a minimum distance apart",
"params": {
"device_a": {"type": "string", "required": True},
"device_b": {"type": "string", "required": True},
"distance": {"type": "float", "required": True, "description": "Min edge-to-edge distance in meters"},
},
"generates": "hard distance_greater_than",
},
"min_spacing": {
"description": "Minimum gap between all device pairs",
"params": {
"min_gap": {"type": "float", "required": False, "default": 0.3, "description": "Minimum gap in meters"},
},
"generates": "hard min_spacing",
},
"workflow_hint": {
"description": "Workflow step order — consecutive devices should be near each other",
"params": {
"workflow": {"type": "string", "required": False, "description": "Workflow name (e.g. 'pcr')"},
"devices": {"type": "list[string]", "required": True, "description": "Ordered device IDs following workflow steps"},
},
"generates": "soft minimize_distance for consecutive pairs + workflow_edges",
},
"face_outward": {
"description": "Devices should face outward from lab center",
"params": {},
"generates": "soft prefer_orientation_mode outward",
},
"face_inward": {
"description": "Devices should face inward toward lab center",
"params": {},
"generates": "soft prefer_orientation_mode inward",
},
"align_cardinal": {
"description": "Devices should align to cardinal directions (0/90/180/270 degrees)",
"params": {},
"generates": "soft prefer_aligned",
},
},
}
# ---------- 优化 API ----------
class DeviceSpec(BaseModel):
id: str
name: str = ""
size: list[float] | None = None
device_type: str = "static"
uuid: str = ""
class ConstraintSpec(BaseModel):
type: str # "hard" or "soft"
rule_name: str
params: dict = {}
weight: float = 1.0
class LabSpec(BaseModel):
width: float
depth: float
obstacles: list[dict] = []
class OptimizeRequest(BaseModel):
devices: list[DeviceSpec]
lab: LabSpec
constraints: list[ConstraintSpec] = []
seeder: str = "compact_outward"
seeder_overrides: dict = {}
run_de: bool = True
workflow_edges: list[list[str]] = []
maxiter: int = 200
seed: int | None = None
snap_cardinal: bool = False
angle_granularity: int | None = None
arm_reach: dict[str, float] = {}
# DE 超参数
strategy: str = "currenttobest1bin"
angle_mode: str = "joint"
mutation: list[float] = [0.5, 1.0]
theta_mutation: list[float] | None = None
recombination: float = 0.7
crossover_mode: str = "device"
class PositionXYZ(BaseModel):
x: float
y: float
z: float
class PlacementResult(BaseModel):
device_id: str
uuid: str
position: PositionXYZ
rotation: PositionXYZ
class OptimizeResponse(BaseModel):
placements: list[PlacementResult]
cost: float
success: bool
seeder_used: str = ""
de_ran: bool = True
@app.post("/optimize", response_model=OptimizeResponse)
async def run_optimize(request: OptimizeRequest):
"""接收设备列表+约束,返回最优布局方案。"""
from fastapi import HTTPException
from .constraints import evaluate_default_hard_constraints, evaluate_constraints
from .mock_checkers import MockCollisionChecker, MockReachabilityChecker
from .optimizer import optimize, snap_theta, snap_theta_safe
from .seeders import resolve_seeder_params, seed_layout
logger.info(
"Optimize request: %d devices, lab %.1f×%.1f, %d constraints, seeder=%s, run_de=%s, angle_granularity=%s",
len(request.devices),
request.lab.width,
request.lab.depth,
len(request.constraints),
request.seeder,
request.run_de,
request.angle_granularity,
)
if request.angle_granularity not in (None, 4, 8, 12, 24):
raise HTTPException(
status_code=400,
detail="angle_granularity must be one of: 4, 8, 12, 24",
)
# 转换输入
devices = create_devices_from_list(
[d.model_dump() for d in request.devices]
)
id_to_catalog = {dev.id: _catalog_id_from_internal(dev.id) for dev in devices}
id_to_uuid = {dev.id: (dev.uuid or dev.id) for dev in devices}
lab = parse_lab(request.lab.model_dump())
constraints = [
Constraint(
type=c.type,
rule_name=c.rule_name,
params=c.params,
weight=c.weight,
)
for c in request.constraints
]
constraints = _expand_constraints_for_duplicates(constraints, devices)
# 1. Resolve seeder
try:
params = resolve_seeder_params(request.seeder, request.seeder_overrides or None)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
# 2. Seed
seed_placements = seed_layout(
devices, lab, params,
request.workflow_edges or None,
)
# 3. Auto-inject alignment soft constraint (opt-in via seeder_overrides)
if request.run_de and seed_placements:
# prefer_aligned: penalize non-cardinal angles默认关闭用户可通过 align_cardinal intent 或 seeder_overrides 开启)
constraints = _maybe_add_prefer_aligned_constraint(
constraints,
request.seeder_overrides.get("align_weight", 0),
)
# 4. Validate DE hyperparameters
if request.strategy not in {"currenttobest1bin", "best1bin", "rand1bin"}:
raise HTTPException(
status_code=400,
detail=f"strategy must be one of: currenttobest1bin, best1bin, rand1bin (got {request.strategy!r})",
)
if request.angle_mode not in {"joint", "hybrid"}:
raise HTTPException(
status_code=400,
detail=f"angle_mode must be one of: joint, hybrid (got {request.angle_mode!r})",
)
if request.crossover_mode not in {"device", "dimension"}:
raise HTTPException(
status_code=400,
detail=f"crossover_mode must be one of: device, dimension (got {request.crossover_mode!r})",
)
if len(request.mutation) != 2 or request.mutation[0] > request.mutation[1]:
raise HTTPException(status_code=400, detail="mutation must be [F_min, F_max] with F_min <= F_max")
if request.mutation[0] < 0 or request.mutation[1] > 2.0:
raise HTTPException(status_code=400, detail="mutation values must be in [0, 2.0]")
if request.theta_mutation is not None:
if len(request.theta_mutation) != 2 or request.theta_mutation[0] > request.theta_mutation[1]:
raise HTTPException(status_code=400, detail="theta_mutation must be [F_min, F_max] with F_min <= F_max")
if request.theta_mutation[0] < 0 or request.theta_mutation[1] > 2.0:
raise HTTPException(status_code=400, detail="theta_mutation values must be in [0, 2.0]")
if not (0 <= request.recombination <= 1.0):
raise HTTPException(status_code=400, detail="recombination must be in [0, 1.0]")
# 5. Conditional Differential Evolution
de_ran = False
checker = MockCollisionChecker()
reachability_checker = MockReachabilityChecker(request.arm_reach or None)
if request.run_de:
result_placements = optimize(
devices=devices,
lab=lab,
constraints=constraints,
collision_checker=checker,
reachability_checker=reachability_checker,
seed_placements=seed_placements,
maxiter=request.maxiter,
seed=request.seed,
strategy=request.strategy,
workflow_edges=request.workflow_edges or None,
angle_granularity=request.angle_granularity,
angle_mode=request.angle_mode,
mutation=tuple(request.mutation),
theta_mutation=tuple(request.theta_mutation) if request.theta_mutation else None,
recombination=request.recombination,
crossover_mode=request.crossover_mode,
)
de_ran = True
else:
result_placements = seed_placements
# 5. θ snap post-processingopt-in默认关闭
if request.snap_cardinal and request.angle_granularity is None:
result_placements = snap_theta_safe(result_placements, devices, lab, checker)
elif request.snap_cardinal and request.angle_granularity is not None:
logger.info(
"snap_cardinal ignored because angle_granularity=%s already constrains theta",
request.angle_granularity,
)
# 6. Evaluate final cost (binary mode for pass/fail reporting)
final_cost = evaluate_default_hard_constraints(
devices, result_placements, lab, checker, graduated=False,
)
# 也检查用户硬约束binary 模式)
if constraints and not math.isinf(final_cost):
user_hard_cost = evaluate_constraints(
devices, result_placements, lab, constraints, checker, reachability_checker,
graduated=False,
)
if math.isinf(user_hard_cost):
final_cost = math.inf
return OptimizeResponse(
placements=[
PlacementResult(
device_id=id_to_catalog.get(p.device_id, p.device_id),
uuid=id_to_uuid.get(p.device_id, p.device_id),
position=PositionXYZ(x=round(p.x, 4), y=round(p.y, 4), z=0.0),
rotation=PositionXYZ(x=0.0, y=0.0, z=round(p.theta, 4)),
)
for p in result_placements
],
cost=final_cost,
success=not math.isinf(final_cost),
seeder_used=request.seeder,
de_ran=de_ran,
)
# ---------- 场景状态 API演示用 ----------
_scene_state: dict = {"version": 0, "placements": []}
_lab_state: dict = {"width": 4.0, "depth": 4.0}
class LabDimensions(BaseModel):
width: float
depth: float
@app.get("/scene/lab")
async def get_lab_dimensions():
"""返回当前实验室尺寸前端推送agent 读取)。"""
return _lab_state
@app.post("/scene/lab")
async def set_lab_dimensions(dims: LabDimensions):
"""前端在加载和尺寸变更时推送。"""
_lab_state["width"] = dims.width
_lab_state["depth"] = dims.depth
return _lab_state
class ScenePlacementsRequest(BaseModel):
placements: list[PlacementResult]
@app.post("/scene/placements")
async def set_scene_placements(request: ScenePlacementsRequest):
"""Agent 写入布局结果,前端轮询读取。"""
_scene_state["version"] += 1
_scene_state["placements"] = [p.model_dump() for p in request.placements]
logger.info(
"Scene placements updated: version=%d, count=%d",
_scene_state["version"],
len(request.placements),
)
return {"version": _scene_state["version"], "count": len(request.placements)}
@app.get("/scene/placements")
async def get_scene_placements():
"""前端轮询此端点,检测 version 变化后应用布局。"""
return _scene_state
@app.delete("/scene/placements")
async def clear_scene_placements():
"""重置场景状态(重录时使用)。"""
_scene_state["version"] = 0
_scene_state["placements"] = []
return {"version": 0, "placements": []}

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@@ -1,7 +0,0 @@
[
{"id": "arm_1", "name": "Elite CS66 Arm", "size": [0.20, 0.20], "device_type": "articulation"},
{"id": "liquid_handler", "name": "Agilent Bravo", "size": [0.80, 0.65], "device_type": "static"},
{"id": "centrifuge", "name": "Centrifuge", "size": [0.50, 0.50], "device_type": "static"},
{"id": "plate_hotel", "name": "Thermo Orbitor RS2", "size": [0.45, 0.55], "device_type": "static"},
{"id": "hplc", "name": "HPLC Station", "size": [0.60, 0.50], "device_type": "static"}
]

View File

@@ -1,7 +0,0 @@
{
"width": 5.0,
"depth": 4.0,
"obstacles": [
{"x": 2.5, "y": 0.0, "width": 0.1, "depth": 0.5}
]
}

View File

@@ -1,241 +0,0 @@
"""Tests for broad_phase.py — 2 轴 sweep-and-prune 宽相碰撞检测。"""
from __future__ import annotations
import math
import random
import pytest
from ..broad_phase import broad_phase_device_pairs, sweep_and_prune_pairs
from ..models import Device, Placement
# ---------------------------------------------------------------------------
# 测试用辅助函数
# ---------------------------------------------------------------------------
def _make_device(device_id: str, w: float = 0.6, d: float = 0.4) -> Device:
"""创建简单测试设备。"""
return Device(id=device_id, name=device_id, bbox=(w, d))
def _make_placement(
device_id: str, x: float, y: float, theta: float = 0.0
) -> Placement:
"""创建简单测试放置。"""
return Placement(device_id=device_id, x=x, y=y, theta=theta)
# ---------------------------------------------------------------------------
# 测试类
# ---------------------------------------------------------------------------
class TestNoOverlap:
"""两台设备距离足够远,宽相不返回候选对。"""
def test_no_overlap_returns_empty(self):
"""水平方向间距远大于 AABB 尺寸 → 0 候选对。"""
devices = [_make_device("A", 1.0, 1.0), _make_device("B", 1.0, 1.0)]
placements = [
_make_placement("A", 0.0, 0.0),
_make_placement("B", 10.0, 0.0),
]
pairs = sweep_and_prune_pairs(devices, placements)
assert pairs == []
class TestOverlapping:
"""两台设备 AABB 明显重叠。"""
def test_overlapping_devices_returned(self):
"""两台 1×1 设备中心距 0.5m → 1 候选对。"""
devices = [_make_device("A", 1.0, 1.0), _make_device("B", 1.0, 1.0)]
placements = [
_make_placement("A", 0.0, 0.0),
_make_placement("B", 0.5, 0.0),
]
pairs = sweep_and_prune_pairs(devices, placements)
assert len(pairs) == 1
assert pairs[0] == (0, 1)
class TestXOverlapYNoOverlap:
"""x 轴投影交叠但 y 轴不交叠,应被 y 轴检查剪枝。"""
def test_x_overlap_y_no_overlap(self):
"""水平接近但垂直方向偏移足够大 → 0 候选对。"""
devices = [_make_device("A", 2.0, 1.0), _make_device("B", 2.0, 1.0)]
placements = [
_make_placement("A", 0.0, 0.0),
_make_placement("B", 0.5, 5.0), # x 轴交叠但 y 轴相距很远
]
pairs = sweep_and_prune_pairs(devices, placements)
assert pairs == []
class TestTouchingDevices:
"""AABB 恰好接触(边缘距离 = 0应作为候选对返回。"""
def test_touching_devices_included(self):
"""两个 1×1 设备中心距恰好为 1.0(半宽 0.5 + 0.5 = 1.0
AABB 边界接触 → 应包含在候选对中(<= 判定)。"""
devices = [_make_device("A", 1.0, 1.0), _make_device("B", 1.0, 1.0)]
placements = [
_make_placement("A", 0.0, 0.0),
_make_placement("B", 1.0, 0.0), # xmax_A = 0.5, xmin_B = 0.5 → 接触
]
pairs = sweep_and_prune_pairs(devices, placements)
# 接触算作潜在碰撞,安全起见需保留
assert len(pairs) == 1
assert pairs[0] == (0, 1)
class TestMultipleDevices:
"""4 台设备验证精确的候选对列表。"""
def test_multiple_devices_correct_pairs(self):
"""排列 4 台设备,只有特定配对 AABB 交叠。
布局1×1 设备):
A(0,0) B(0.8,0) — A-B 交叠(中心距 0.8 < 1.0
C(0,5) — 远离 A、B
D(0.9,5) — C-D 交叠(中心距 0.9 < 1.0
期望候选对: (A,B) 和 (C,D)。
"""
devices = [
_make_device("A", 1.0, 1.0),
_make_device("B", 1.0, 1.0),
_make_device("C", 1.0, 1.0),
_make_device("D", 1.0, 1.0),
]
placements = [
_make_placement("A", 0.0, 0.0),
_make_placement("B", 0.8, 0.0),
_make_placement("C", 0.0, 5.0),
_make_placement("D", 0.9, 5.0),
]
pairs = sweep_and_prune_pairs(devices, placements)
pair_set = set(pairs)
assert (0, 1) in pair_set # A-B
assert (2, 3) in pair_set # C-D
assert len(pair_set) == 2
class TestRotatedDeviceAabb:
"""旋转设备导致 AABB 变大,命中候选对。"""
def test_rotated_device_aabb(self):
"""一台窄长设备 (2.0×0.2)
- 未旋转时 AABB 半宽 = 1.0,两设备中心距 2.5 → 不交叠
- 旋转 90° 后 AABB 半宽 = 0.1,半深 = 1.0 → 仍不交叠
- 旋转 45° 后 AABB 半宽 ≈ (2*cos45 + 0.2*sin45)/2 ≈ 0.778
但另一台放在 x=1.6,半宽 = 1.0
所以 xmax_A = 0 + 0.778 = 0.778 < 1.6 - 1.0 = 0.6 → 不够
更好的方案:用中心距 1.5,未旋转时不交叠,旋转后交叠。
未旋转: half_w_A = 0.3 (bbox 0.6x2.0), half_w_B = 0.3
A: xmax = 0 + 0.3 = 0.3, B: xmin = 1.5 - 0.3 = 1.2 → 不交叠
旋转 90°: A 的 bbox (0.6, 2.0) → half_w = (0.6*0 + 2.0*1)/2 = 1.0
A: xmax = 0 + 1.0 = 1.0, B: xmin = 1.5 - 0.3 = 1.2 → 仍不交叠
用 bbox (0.4, 2.0),间距 1.2
未旋转: half_w = 0.2, xmax_A = 0.2, xmin_B = 1.2 - 0.2 = 1.0 → 不交叠
旋转 45°: half_w = (0.4*cos45 + 2.0*sin45)/2 = (0.283+1.414)/2 = 0.849
xmax_A = 0.849, xmin_B = 1.2 - 0.2 = 1.0 → 不交叠
间距 0.8
未旋转: xmax_A = 0.2, xmin_B = 0.8 - 0.2 = 0.6 → 不交叠 ✓
旋转 45°: xmax_A = 0.849, xmin_B = 0.6 → 交叠 ✓ (0.849 > 0.6)
y 轴: half_d_A_rot = (0.4*sin45 + 2.0*cos45)/2 = 0.849, half_d_B = 1.0
ymax_A = 0.849, ymin_B = -1.0 → 交叠 ✓
"""
dev_narrow = _make_device("narrow", 0.4, 2.0)
dev_normal = _make_device("normal", 0.4, 2.0)
# 未旋转:不交叠
placements_no_rot = [
_make_placement("narrow", 0.0, 0.0, theta=0.0),
_make_placement("normal", 0.8, 0.0, theta=0.0),
]
assert sweep_and_prune_pairs([dev_narrow, dev_normal], placements_no_rot) == []
# narrow 旋转 45° → AABB 变大 → 交叠
placements_rot = [
_make_placement("narrow", 0.0, 0.0, theta=math.pi / 4),
_make_placement("normal", 0.8, 0.0, theta=0.0),
]
pairs = sweep_and_prune_pairs([dev_narrow, dev_normal], placements_rot)
assert len(pairs) == 1
class TestOriginalIndices:
"""验证返回的索引对应 placements 原始顺序而非排序后顺序。"""
def test_sorted_output_preserves_original_indices(self):
"""故意让 placements 按 x 坐标逆序排列,
验证返回的索引仍是原始顺序。"""
devices = [
_make_device("A", 1.0, 1.0),
_make_device("B", 1.0, 1.0),
_make_device("C", 1.0, 1.0),
]
# 逆序排列C 在最左A 在最右
placements = [
_make_placement("A", 5.0, 0.0), # idx 0, 最右
_make_placement("B", 4.5, 0.0), # idx 1, 中间(与 A 交叠)
_make_placement("C", 0.0, 0.0), # idx 2, 最左(独立)
]
pairs = sweep_and_prune_pairs(devices, placements)
# A(idx=0) 和 B(idx=1) AABB 交叠,索引应为 (0, 1)
assert len(pairs) == 1
assert pairs[0] == (0, 1)
# 同时验证 broad_phase_device_pairs 返回正确 device_id
id_pairs = broad_phase_device_pairs(devices, placements)
assert id_pairs == [("A", "B")]
class TestPairCountReduction:
"""大规模随机测试:宽相候选对数应远小于 N*(N-1)/2。"""
def test_pair_count_reduction(self):
"""N=15 台设备随机放置在 10×10 实验室 → 候选对数显著少于全量。"""
random.seed(42)
n = 15
devices = [_make_device(f"D{i}", 0.5, 0.5) for i in range(n)]
placements = [
_make_placement(f"D{i}", random.uniform(0, 10), random.uniform(0, 10))
for i in range(n)
]
pairs = sweep_and_prune_pairs(devices, placements)
full_pairs = n * (n - 1) // 2 # = 105
# 在 10×10 区域放 15 台 0.5×0.5 设备,交叠率应很低
assert len(pairs) < full_pairs
# 额外断言:候选对数不超过全量的一半(保守判定)
assert len(pairs) < full_pairs * 0.5
class TestEdgeCases:
"""边界情况。"""
def test_empty_input(self):
"""空列表 → 空结果。"""
assert sweep_and_prune_pairs([], []) == []
def test_single_device(self):
"""单台设备 → 无候选对。"""
devices = [_make_device("A")]
placements = [_make_placement("A", 0.0, 0.0)]
assert sweep_and_prune_pairs(devices, placements) == []
def test_identical_positions(self):
"""两台设备完全重叠 → 1 候选对。"""
devices = [_make_device("A", 1.0, 1.0), _make_device("B", 1.0, 1.0)]
placements = [
_make_placement("A", 0.0, 0.0),
_make_placement("B", 0.0, 0.0),
]
pairs = sweep_and_prune_pairs(devices, placements)
assert len(pairs) == 1

View File

@@ -1,435 +0,0 @@
"""Regression tests for V2 Stage 1 bugfixes.
Covers:
- Duplicate device ID stacking (catalog ID + #N internal IDs)
- DE orientation preservation (prefer_orientation_mode constraint)
- prefer_aligned auto-injection and adjustability
- Preset switch reorientation
- min_spacing with duplicate catalog IDs
"""
import math
import pytest
from ..constraints import evaluate_constraints
from ..mock_checkers import MockCollisionChecker
from ..models import Constraint, Device, Lab, Opening, Placement
from ..obb import obb_corners, obb_overlap
from ..optimizer import (
_placements_to_vector,
_vector_to_placements,
optimize,
snap_theta,
)
from ..seeders import resolve_seeder_params, seed_layout
# ── Helpers ─────────────────────────────────────────────
def _ot(uid: str) -> Device:
return Device(
id=uid, name="Opentrons Liquid Handler",
bbox=(0.6243, 0.5672), openings=[Opening(direction=(0.0, -1.0))],
)
def _tecan(uid: str) -> Device:
return Device(
id=uid, name="Tecan EVO 100",
bbox=(0.8121, 0.8574), openings=[Opening(direction=(0.0, -1.0))],
)
def _facing_dot(p: Placement, device: Device, lab: Lab) -> float:
"""Dot product of rotated front vector with vector from center to device.
Positive = outward, negative = inward."""
cx, cy = lab.width / 2, lab.depth / 2
dx, dy = p.x - cx, p.y - cy
front = device.openings[0].direction if device.openings else (0.0, -1.0)
rf_x = math.cos(p.theta) * front[0] - math.sin(p.theta) * front[1]
rf_y = math.sin(p.theta) * front[0] + math.cos(p.theta) * front[1]
return rf_x * dx + rf_y * dy
def _has_collision(devices, placements):
for i in range(len(devices)):
for j in range(i + 1, len(devices)):
ci = obb_corners(placements[i].x, placements[i].y,
devices[i].bbox[0], devices[i].bbox[1], placements[i].theta)
cj = obb_corners(placements[j].x, placements[j].y,
devices[j].bbox[0], devices[j].bbox[1], placements[j].theta)
if obb_overlap(ci, cj):
return True
return False
# ── Bug 1: Duplicate device ID stacking ────────────────
class TestDuplicateDeviceIDs:
"""When two instances of the same catalog device are placed,
unique uuid-based IDs must prevent dict-key collisions."""
def test_vector_roundtrip_preserves_unique_positions(self):
"""_placements_to_vector → _vector_to_placements with unique IDs."""
devices = [_ot("uuid-a"), _ot("uuid-b")]
placements = [
Placement(device_id="uuid-a", x=0.5, y=0.5, theta=0.0),
Placement(device_id="uuid-b", x=1.5, y=1.5, theta=1.0),
]
vec = _placements_to_vector(placements, devices)
decoded = _vector_to_placements(vec, devices)
assert decoded[0].x == pytest.approx(0.5)
assert decoded[1].x == pytest.approx(1.5)
def test_min_spacing_detects_stacked_unique_ids(self):
"""min_spacing should detect two devices at the same position
when they have unique IDs."""
devices = [_ot("uuid-a"), _ot("uuid-b")]
stacked = [
Placement(device_id="uuid-a", x=1.0, y=1.0, theta=0.0),
Placement(device_id="uuid-b", x=1.0, y=1.0, theta=0.0),
]
lab = Lab(width=5, depth=5)
constraints = [Constraint(type="hard", rule_name="min_spacing",
params={"min_gap": 0.05})]
# graduated=True (default): 返回有限惩罚
cost = evaluate_constraints(devices, stacked, lab, constraints,
MockCollisionChecker())
assert cost > 0
assert not math.isinf(cost)
# graduated=False: binary inf
cost_binary = evaluate_constraints(devices, stacked, lab, constraints,
MockCollisionChecker(),
graduated=False)
assert math.isinf(cost_binary)
def test_create_devices_uses_catalog_id_with_suffixes(self):
"""create_devices_from_list should keep catalog IDs and suffix duplicates."""
from ..device_catalog import create_devices_from_list
specs = [
{"id": "opentrons_liquid_handler", "uuid": "abc-123"},
{"id": "opentrons_liquid_handler", "uuid": "def-456"},
]
devices = create_devices_from_list(specs)
assert devices[0].id == "opentrons_liquid_handler"
assert devices[1].id == "opentrons_liquid_handler#2"
assert devices[0].uuid == "abc-123"
assert devices[1].uuid == "def-456"
# Both should have the same bbox from footprints
assert devices[0].bbox == devices[1].bbox
def test_create_devices_fallback_no_uuid(self):
"""Without uuid, Device.id falls back to catalog id."""
from ..device_catalog import create_devices_from_list
specs = [{"id": "opentrons_liquid_handler"}]
devices = create_devices_from_list(specs)
assert devices[0].id == "opentrons_liquid_handler"
# ── Bug 2 & 4: DE orientation preservation ─────────────
class TestOrientationWithDE:
"""DE must preserve seeder orientation direction (outward/inward)
via the prefer_orientation_mode constraint."""
def _run_de_with_orientation(self, mode, seed_val=42):
devices = [_ot("ot1"), _ot("ot2"), _tecan("tecan")]
lab = Lab(width=2.0, depth=2.0)
params = resolve_seeder_params(
"compact_outward" if mode == "outward" else "spread_inward"
)
seed = seed_layout(devices, lab, params)
constraints = [
Constraint(type="hard", rule_name="min_spacing",
params={"min_gap": 0.05}),
Constraint(type="soft", rule_name="prefer_orientation_mode",
params={"mode": mode}, weight=5.0),
Constraint(type="soft", rule_name="prefer_aligned", weight=2.0),
]
result = optimize(devices, lab, constraints, seed_placements=seed,
maxiter=200, seed=seed_val)
result = snap_theta(result)
return devices, lab, result
def test_compact_outward_de_faces_outward(self):
devices, lab, result = self._run_de_with_orientation("outward")
for i, p in enumerate(result):
dot = _facing_dot(p, devices[i], lab)
assert dot > 0, (
f"{p.device_id} faces inward (dot={dot:.3f}) "
f"at ({p.x:.2f},{p.y:.2f}) theta={math.degrees(p.theta):.0f}°"
)
def test_spread_inward_de_faces_inward(self):
devices, lab, result = self._run_de_with_orientation("inward")
for i, p in enumerate(result):
dot = _facing_dot(p, devices[i], lab)
assert dot < 0, (
f"{p.device_id} faces outward (dot={dot:.3f}) "
f"at ({p.x:.2f},{p.y:.2f}) theta={math.degrees(p.theta):.0f}°"
)
def test_switching_preset_changes_orientation(self):
"""Switching from outward to inward should produce opposite facing."""
_, lab, out_result = self._run_de_with_orientation("outward")
devices_in, _, in_result = self._run_de_with_orientation("inward")
# At least one device should have different facing
out_dots = [_facing_dot(p, devices_in[i], lab) for i, p in enumerate(out_result)]
in_dots = [_facing_dot(p, devices_in[i], lab) for i, p in enumerate(in_result)]
# Outward: all positive; inward: all negative
assert all(d > 0 for d in out_dots), f"outward dots: {out_dots}"
assert all(d < 0 for d in in_dots), f"inward dots: {in_dots}"
def test_no_collision_after_de(self):
devices, lab, result = self._run_de_with_orientation("outward")
assert not _has_collision(devices, result)
# ── Bug 3: prefer_aligned & prefer_orientation_mode ────
class TestOrientationConstraints:
"""Test the new constraint rules directly."""
def test_prefer_orientation_mode_outward_zero_at_correct(self):
"""Zero cost when device faces outward from center."""
device = _ot("a")
# Device to the right of center, front pointing right
# front=(0,-1), theta=pi/2 → rotated front = (1, 0) = rightward
lab = Lab(width=4, depth=4)
placements = [Placement("a", 3.0, 2.0, math.pi / 2)]
constraint = Constraint(
type="soft", rule_name="prefer_orientation_mode",
params={"mode": "outward"}, weight=1.0,
)
cost = evaluate_constraints(
[device], placements, lab, [constraint], MockCollisionChecker(),
)
assert cost == pytest.approx(0.0, abs=0.01)
def test_prefer_orientation_mode_outward_penalty_at_inward(self):
"""High cost when device faces inward (opposite of outward)."""
device = _ot("a")
# Device to the right of center, front pointing left (inward)
# front=(0,-1), theta=3*pi/2 → rotated front = (-1, 0) = leftward
lab = Lab(width=4, depth=4)
placements = [Placement("a", 3.0, 2.0, 3 * math.pi / 2)]
constraint = Constraint(
type="soft", rule_name="prefer_orientation_mode",
params={"mode": "outward"}, weight=1.0,
)
cost = evaluate_constraints(
[device], placements, lab, [constraint], MockCollisionChecker(),
)
# 180° off → (1 - cos(pi)) / 2 = 1.0
assert cost == pytest.approx(1.0, abs=0.05)
def test_prefer_orientation_mode_inward(self):
"""Zero cost when device faces inward."""
device = _ot("a")
# Device to the right of center, front pointing left (inward)
lab = Lab(width=4, depth=4)
placements = [Placement("a", 3.0, 2.0, 3 * math.pi / 2)]
constraint = Constraint(
type="soft", rule_name="prefer_orientation_mode",
params={"mode": "inward"}, weight=1.0,
)
cost = evaluate_constraints(
[device], placements, lab, [constraint], MockCollisionChecker(),
)
assert cost == pytest.approx(0.0, abs=0.01)
def test_prefer_seeder_orientation_zero_at_target(self):
"""Zero cost when theta matches target."""
device = Device(id="a", name="A", bbox=(0.5, 0.5))
lab = Lab(width=4, depth=4)
placements = [Placement("a", 2, 2, 1.5)]
constraint = Constraint(
type="soft", rule_name="prefer_seeder_orientation",
params={"target_thetas": {"a": 1.5}}, weight=1.0,
)
cost = evaluate_constraints(
[device], placements, lab, [constraint], MockCollisionChecker(),
)
assert cost == pytest.approx(0.0, abs=1e-9)
def test_prefer_seeder_orientation_penalty_at_deviation(self):
"""Non-zero cost when theta deviates from target."""
device = Device(id="a", name="A", bbox=(0.5, 0.5))
lab = Lab(width=4, depth=4)
placements = [Placement("a", 2, 2, math.pi)] # pi away from 0
constraint = Constraint(
type="soft", rule_name="prefer_seeder_orientation",
params={"target_thetas": {"a": 0.0}}, weight=1.0,
)
cost = evaluate_constraints(
[device], placements, lab, [constraint], MockCollisionChecker(),
)
# (1 - cos(pi)) / 2 = 1.0
assert cost == pytest.approx(1.0)
# ── API endpoint regression ────────────────────────────
class TestEndpointOrientation:
"""Test that /optimize injects orientation constraints."""
def test_endpoint_with_de_injects_orientation(self):
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
resp = client.post("/optimize", json={
"devices": [
{"id": "opentrons_liquid_handler", "uuid": "u1"},
{"id": "opentrons_liquid_handler", "uuid": "u2"},
],
"lab": {"width": 3, "depth": 3},
"seeder": "compact_outward",
"run_de": True,
"maxiter": 50,
"seed": 42,
})
assert resp.status_code == 200
data = resp.json()
# Both devices should have unique uuids in response
uuids = [p["uuid"] for p in data["placements"]]
assert len(set(uuids)) == 2, f"Expected 2 unique uuids, got {uuids}"
def test_endpoint_orientation_weight_override(self):
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
resp = client.post("/optimize", json={
"devices": [{"id": "opentrons_liquid_handler", "uuid": "u1"}],
"lab": {"width": 3, "depth": 3},
"seeder": "compact_outward",
"seeder_overrides": {"orientation_weight": 10, "align_weight": 0},
"run_de": True,
"maxiter": 50,
"seed": 42,
})
assert resp.status_code == 200
def test_endpoint_align_weight_zero_disables(self):
"""Setting align_weight=0 should not inject prefer_aligned."""
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
resp = client.post("/optimize", json={
"devices": [{"id": "opentrons_liquid_handler", "uuid": "u1"}],
"lab": {"width": 3, "depth": 3},
"seeder": "compact_outward",
"seeder_overrides": {"align_weight": 0},
"run_de": True,
"maxiter": 50,
"seed": 42,
})
assert resp.status_code == 200
# ── Broader scenario tests ─────────────────────────────
class TestScenarios:
"""End-to-end scenarios similar to user's real usage."""
def test_user_scenario_2ot_1tecan_compact_outward(self):
"""User's exact scenario: 2 OT + 1 Tecan in 2m×2m, compact outward."""
devices = [_ot("ot1"), _ot("ot2"), _tecan("tecan")]
lab = Lab(width=2.0, depth=2.0)
params = resolve_seeder_params("compact_outward")
seed = seed_layout(devices, lab, params)
constraints = [
Constraint(type="hard", rule_name="min_spacing",
params={"min_gap": 0.05}),
Constraint(type="soft", rule_name="prefer_orientation_mode",
params={"mode": "outward"}, weight=5.0),
Constraint(type="soft", rule_name="prefer_aligned", weight=2.0),
]
result = optimize(devices, lab, constraints, seed_placements=seed,
maxiter=200, seed=42)
result = snap_theta(result)
# No stacking
assert not _has_collision(devices, result)
# All outward
for i, p in enumerate(result):
assert _facing_dot(p, devices[i], lab) > 0
def test_4_medium_devices_mixed_openings(self):
"""4 devices with different opening directions."""
devices = [
Device(id="d0", name="D0", bbox=(0.5, 0.3), openings=[Opening((1, 0))]),
Device(id="d1", name="D1", bbox=(0.5, 0.3), openings=[Opening((-1, 0))]),
Device(id="d2", name="D2", bbox=(0.5, 0.3), openings=[Opening((0, -1))]),
Device(id="d3", name="D3", bbox=(0.5, 0.3), openings=[Opening((0, 1))]),
]
lab = Lab(width=3.0, depth=3.0)
params = resolve_seeder_params("compact_outward")
seed = seed_layout(devices, lab, params)
constraints = [
Constraint(type="hard", rule_name="min_spacing",
params={"min_gap": 0.05}),
Constraint(type="soft", rule_name="prefer_orientation_mode",
params={"mode": "outward"}, weight=5.0),
Constraint(type="soft", rule_name="prefer_aligned", weight=2.0),
]
result = optimize(devices, lab, constraints, seed_placements=seed,
maxiter=200, seed=42)
result = snap_theta(result)
assert not _has_collision(devices, result)
for i, p in enumerate(result):
assert _facing_dot(p, devices[i], lab) > 0
# ── V2 Stage 1: 默认关闭 cardinal snap/alignment ────────
class TestV2Stage1Bugfixes:
"""align_weight 默认为 0snap_cardinal 默认关闭。"""
def test_default_align_weight_is_zero(self):
"""Default request (no seeder_overrides) should NOT inject prefer_aligned."""
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
resp = client.post("/optimize", json={
"devices": [{"id": "opentrons_liquid_handler", "uuid": "u1"}],
"lab": {"width": 3, "depth": 3},
"seeder": "compact_outward",
"run_de": True,
"maxiter": 50,
"seed": 42,
})
assert resp.status_code == 200
def test_snap_cardinal_off_by_default(self):
"""Default request should NOT snap theta to cardinal."""
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
resp = client.post("/optimize", json={
"devices": [{"id": "opentrons_liquid_handler", "uuid": "u1"}],
"lab": {"width": 3, "depth": 3},
"seeder": "compact_outward",
"run_de": True,
"maxiter": 10,
"seed": 42,
})
assert resp.status_code == 200
def test_snap_cardinal_opt_in(self):
"""snap_cardinal=True should be accepted and snap angles."""
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
resp = client.post("/optimize", json={
"devices": [{"id": "opentrons_liquid_handler", "uuid": "u1"}],
"lab": {"width": 3, "depth": 3},
"seeder": "compact_outward",
"snap_cardinal": True,
"run_de": True,
"maxiter": 10,
"seed": 42,
})
assert resp.status_code == 200

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@@ -1,505 +0,0 @@
"""约束体系测试。"""
import math
import pytest
from ..constraints import (
_crossing_penalty,
_opening_surface_center,
DEFAULT_WEIGHT_DISTANCE,
evaluate_constraints,
evaluate_default_hard_constraints,
)
from ..mock_checkers import MockCollisionChecker, MockReachabilityChecker
from ..models import Constraint, Device, Opening, Placement, Lab
from ..obb import nearest_point_on_obb, obb_corners
def _make_devices():
return [
Device(id="a", name="Device A", bbox=(0.5, 0.5)),
Device(id="b", name="Device B", bbox=(0.5, 0.5)),
]
def _make_lab():
return Lab(width=5.0, depth=4.0)
class TestDefaultHardConstraints:
def test_no_collision_passes(self):
"""无碰撞的布局应返回 0。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 1.0, 0.0),
Placement("b", 3.0, 3.0, 0.0),
]
checker = MockCollisionChecker()
cost = evaluate_default_hard_constraints(devices, placements, _make_lab(), checker)
assert cost == 0.0
def test_collision_returns_graduated_penalty(self):
"""碰撞布局应返回正的graduated penalty非inf"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 1.0, 0.0),
Placement("b", 1.2, 1.0, 0.0),
]
checker = MockCollisionChecker()
cost = evaluate_default_hard_constraints(devices, placements, _make_lab(), checker)
assert cost > 0
assert not math.isinf(cost)
def test_collision_returns_inf_binary_mode(self):
"""Binary mode: 碰撞布局应返回 inf。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 1.0, 0.0),
Placement("b", 1.2, 1.0, 0.0),
]
checker = MockCollisionChecker()
cost = evaluate_default_hard_constraints(
devices, placements, _make_lab(), checker, graduated=False,
)
assert math.isinf(cost)
def test_out_of_bounds_returns_graduated_penalty(self):
"""越界布局应返回正的graduated penalty非inf"""
devices = _make_devices()
placements = [
Placement("a", 0.1, 0.1, 0.0), # 左下角越界
Placement("b", 3.0, 3.0, 0.0),
]
checker = MockCollisionChecker()
cost = evaluate_default_hard_constraints(devices, placements, _make_lab(), checker)
assert cost > 0
assert not math.isinf(cost)
def test_out_of_bounds_returns_inf_binary_mode(self):
"""Binary mode: 越界布局应返回 inf。"""
devices = _make_devices()
placements = [
Placement("a", 0.1, 0.1, 0.0),
Placement("b", 3.0, 3.0, 0.0),
]
checker = MockCollisionChecker()
cost = evaluate_default_hard_constraints(
devices, placements, _make_lab(), checker, graduated=False,
)
assert math.isinf(cost)
def test_worse_collision_higher_cost(self):
"""Deeper penetration should produce higher cost."""
devices = _make_devices()
checker = MockCollisionChecker()
lab = _make_lab()
# Small overlap
cost_small = evaluate_default_hard_constraints(
devices, [Placement("a", 1.0, 1.0, 0.0), Placement("b", 1.4, 1.0, 0.0)],
lab, checker,
)
# Large overlap
cost_large = evaluate_default_hard_constraints(
devices, [Placement("a", 1.0, 1.0, 0.0), Placement("b", 1.1, 1.0, 0.0)],
lab, checker,
)
assert cost_large > cost_small > 0
class TestUserConstraints:
def test_distance_less_than_satisfied(self):
"""距离约束满足时 cost=0。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 1.0, 0.0),
Placement("b", 1.5, 1.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="distance_less_than",
params={"device_a": "a", "device_b": "b", "distance": 1.0})
]
checker = MockCollisionChecker()
reachability = MockReachabilityChecker()
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker, reachability
)
assert cost == 0.0
def test_distance_less_than_violated_hard(self):
"""硬距离约束违反graduated模式返回有限惩罚binary模式返回inf。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 1.0, 0.0),
Placement("b", 4.0, 3.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="distance_less_than",
params={"device_a": "a", "device_b": "b", "distance": 1.0})
]
checker = MockCollisionChecker()
# graduated=True (default): 有限惩罚
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker
)
assert cost > 0
assert not math.isinf(cost)
# graduated=False: binary inf
cost_binary = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker,
graduated=False,
)
assert math.isinf(cost_binary)
def test_minimize_distance_cost(self):
"""minimize_distance 约束应返回正比于距离的 cost。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 1.0, 0.0),
Placement("b", 3.0, 1.0, 0.0),
]
constraints = [
Constraint(type="soft", rule_name="minimize_distance",
params={"device_a": "a", "device_b": "b"}, weight=2.0)
]
checker = MockCollisionChecker()
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker
)
# edge-to-edge distance = 2.0 - 0.25 - 0.25 = 1.5, weight = 2.0 → cost = 3.0
assert abs(cost - 3.0) < 0.01
def test_reachability_constraint(self):
"""可达性约束:目标在臂展内应通过(不返回 inf
Opening-faces-arm penalty may add a small soft cost when the
target's opening doesn't face the arm, but it must not cause
hard failure (inf).
"""
devices = [
Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
Device(id="target", name="Target", bbox=(0.5, 0.5)),
]
placements = [
Placement("arm", 1.0, 1.0, 0.0),
Placement("target", 1.5, 1.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="reachability",
params={"arm_id": "arm", "target_device_id": "target"})
]
checker = MockCollisionChecker()
reachability = MockReachabilityChecker(arm_reach={"arm": 1.0})
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker, reachability
)
assert not math.isinf(cost) # reachable → no hard failure
def test_reachability_constraint_violated(self):
"""可达性约束:目标超出臂展 — graduated返回有限惩罚binary返回inf。"""
devices = [
Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
Device(id="target", name="Target", bbox=(0.5, 0.5)),
]
placements = [
Placement("arm", 1.0, 1.0, 0.0),
Placement("target", 4.0, 3.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="reachability",
params={"arm_id": "arm", "target_device_id": "target"})
]
checker = MockCollisionChecker()
reachability = MockReachabilityChecker(arm_reach={"arm": 1.0})
# graduated=True (default): 有限惩罚
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker, reachability
)
assert cost > 0
assert not math.isinf(cost)
# graduated=False: binary inf
cost_binary = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker, reachability,
graduated=False,
)
assert math.isinf(cost_binary)
def test_distance_less_than_uses_edge_to_edge():
"""distance_less_than should measure edge-to-edge, not center-to-center.
Two devices: centers 3m apart, each 2m wide → edge gap = 1m.
Constraint: distance_less_than 1.5m (edge-to-edge).
Old center-to-center: 3m > 1.5m → violation.
New edge-to-edge: 1m < 1.5m → satisfied.
"""
devices = [
Device(id="a", name="A", bbox=(2.0, 1.0)),
Device(id="b", name="B", bbox=(2.0, 1.0)),
]
placements = [
Placement(device_id="a", x=1.0, y=1.0, theta=0.0),
Placement(device_id="b", x=4.0, y=1.0, theta=0.0),
]
lab = Lab(width=10, depth=10)
constraint = Constraint(
type="soft", rule_name="distance_less_than",
params={"device_a": "a", "device_b": "b", "distance": 1.5},
weight=1.0,
)
checker = MockCollisionChecker()
cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
assert cost == pytest.approx(0.0)
def test_prefer_aligned_zero_at_cardinal():
"""prefer_aligned cost = 0 when all devices at 0/90/180/270°."""
devices = [Device(id="a", name="A", bbox=(1.0, 1.0))]
lab = Lab(width=10, depth=10)
checker = MockCollisionChecker()
for angle in [0, math.pi / 2, math.pi, 3 * math.pi / 2]:
placements = [Placement(device_id="a", x=5, y=5, theta=angle)]
constraint = Constraint(type="soft", rule_name="prefer_aligned", weight=1.0)
cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
assert cost == pytest.approx(0.0, abs=1e-9)
def test_prefer_aligned_max_at_45():
"""prefer_aligned cost is maximum when device at 45°."""
devices = [Device(id="a", name="A", bbox=(1.0, 1.0))]
placements = [Placement(device_id="a", x=5, y=5, theta=math.pi / 4)]
lab = Lab(width=10, depth=10)
constraint = Constraint(type="soft", rule_name="prefer_aligned", weight=1.0)
checker = MockCollisionChecker()
cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
# (1 - cos(4 * pi/4)) / 2 = (1 - cos(pi)) / 2 = (1 - (-1)) / 2 = 1.0
assert cost == pytest.approx(1.0)
def test_prefer_aligned_sums_over_devices():
"""Cost sums across all devices."""
devices = [
Device(id="a", name="A", bbox=(1.0, 1.0)),
Device(id="b", name="B", bbox=(1.0, 1.0)),
]
placements = [
Placement(device_id="a", x=2, y=2, theta=math.pi / 4), # cost = 1.0
Placement(device_id="b", x=7, y=7, theta=math.pi / 4), # cost = 1.0
]
lab = Lab(width=10, depth=10)
constraint = Constraint(type="soft", rule_name="prefer_aligned", weight=2.0)
checker = MockCollisionChecker()
cost = evaluate_constraints(devices, placements, lab, [constraint], checker)
# 2 devices × 1.0 × weight 2.0 = 4.0
assert cost == pytest.approx(4.0)
class TestGraduatedHardConstraints:
"""graduated 模式下硬约束返回比例惩罚而非 inf。"""
def test_hard_reachability_graduated_finite(self):
"""graduated=True: 硬可达性返回有限惩罚。"""
devices = [
Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
Device(id="t", name="Target", bbox=(0.5, 0.5)),
]
placements = [
Placement("arm", 1.0, 1.0, 0.0),
Placement("t", 4.0, 3.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="reachability",
params={"arm_id": "arm", "target_device_id": "t"}, weight=1.0)
]
checker = MockCollisionChecker()
reach = MockReachabilityChecker(arm_reach={"arm": 1.0})
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker, reach,
graduated=True,
)
assert cost > 0
assert not math.isinf(cost)
def test_hard_reachability_binary_inf(self):
"""graduated=False: 硬可达性返回 inf。"""
devices = [
Device(id="arm", name="Arm", bbox=(0.2, 0.2), device_type="articulation"),
Device(id="t", name="Target", bbox=(0.5, 0.5)),
]
placements = [
Placement("arm", 1.0, 1.0, 0.0),
Placement("t", 4.0, 3.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="reachability",
params={"arm_id": "arm", "target_device_id": "t"}, weight=1.0)
]
checker = MockCollisionChecker()
reach = MockReachabilityChecker(arm_reach={"arm": 1.0})
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker, reach,
graduated=False,
)
assert math.isinf(cost)
def test_hard_min_spacing_graduated_sums_all_pairs(self):
"""graduated模式min_spacing 对所有违规对求和(不只第一对)。"""
devices = [
Device(id="a", name="A", bbox=(0.5, 0.5)),
Device(id="b", name="B", bbox=(0.5, 0.5)),
Device(id="c", name="C", bbox=(0.5, 0.5)),
]
# 三个设备间距都小于 min_gap=1.0
placements = [
Placement("a", 1.0, 2.0, 0.0),
Placement("b", 1.3, 2.0, 0.0), # OBB 边缘距 a 约 0.3
Placement("c", 1.6, 2.0, 0.0), # OBB 边缘距 b 约 0.3, 距 a 约 0.6
]
constraints = [
Constraint(type="hard", rule_name="min_spacing",
params={"min_gap": 1.0}, weight=1.0)
]
checker = MockCollisionChecker()
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker,
graduated=True,
)
# 应大于 0 且有限(累加多对违规)
assert cost > 0
assert not math.isinf(cost)
def test_hard_min_spacing_binary_inf(self):
"""graduated=False: min_spacing 违规返回 inf。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 2.0, 0.0),
Placement("b", 1.3, 2.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="min_spacing",
params={"min_gap": 1.0}, weight=1.0)
]
checker = MockCollisionChecker()
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker,
graduated=False,
)
assert math.isinf(cost)
def test_hard_distance_less_than_graduated(self):
"""graduated模式distance_less_than 硬约束返回比例惩罚。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 2.0, 0.0),
Placement("b", 4.0, 2.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="distance_less_than",
params={"device_a": "a", "device_b": "b", "distance": 0.5},
weight=2.0)
]
checker = MockCollisionChecker()
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker,
graduated=True,
)
# HARD_MULTIPLIER(5) × weight(2) × overshoot > 0
assert cost > 0
assert not math.isinf(cost)
def test_graduated_default_is_true(self):
"""不传 graduated 参数时默认使用 graduated 模式。"""
devices = _make_devices()
placements = [
Placement("a", 1.0, 2.0, 0.0),
Placement("b", 4.0, 2.0, 0.0),
]
constraints = [
Constraint(type="hard", rule_name="distance_less_than",
params={"device_a": "a", "device_b": "b", "distance": 0.5},
weight=1.0)
]
checker = MockCollisionChecker()
# 不指定 graduated — 默认应为 True → 有限惩罚
cost = evaluate_constraints(
devices, placements, _make_lab(), constraints, checker,
)
assert not math.isinf(cost)
class TestCrossingPenalty:
"""_crossing_penalty: 交叉长度加权的 soft penalty。"""
def _make_device(self, dev_id, bbox=(0.5, 0.5), direction=(0.0, -1.0)):
return Device(
id=dev_id, name=dev_id, device_type="static",
bbox=bbox, height=0.3,
openings=[Opening(direction=direction, label="front")],
)
def test_no_blockers_returns_zero(self):
"""arm 与 target 之间无遮挡设备 → 交叉代价为 0。"""
arm = self._make_device("arm", bbox=(2.14, 0.35))
target = self._make_device("target")
arm_p = Placement(device_id="arm", x=2.0, y=1.0, theta=0.0)
target_p = Placement(device_id="target", x=0.5, y=1.0, theta=3.14159)
device_map = {"arm": arm, "target": target}
placement_map = {"arm": arm_p, "target": target_p}
opening_pt = _opening_surface_center(target, target_p)
arm_corners = obb_corners(arm_p.x, arm_p.y, arm.bbox[0], arm.bbox[1], arm_p.theta)
nearest = nearest_point_on_obb(opening_pt[0], opening_pt[1], arm_corners)
cost = _crossing_penalty(
opening_pt, nearest,
"arm", "target",
device_map, placement_map,
)
assert cost == 0.0
def test_one_blocker_proportional_to_length(self):
"""一个遮挡设备 → cost = DEFAULT_WEIGHT_DISTANCE * 穿过长度。"""
arm = self._make_device("arm", bbox=(2.14, 0.35))
target = self._make_device("target")
blocker = self._make_device("blocker", bbox=(0.5, 0.5))
arm_p = Placement(device_id="arm", x=3.0, y=1.0, theta=0.0)
target_p = Placement(device_id="target", x=0.0, y=1.0, theta=0.0)
blocker_p = Placement(device_id="blocker", x=1.5, y=1.0, theta=0.0)
device_map = {"arm": arm, "target": target, "blocker": blocker}
placement_map = {"arm": arm_p, "target": target_p, "blocker": blocker_p}
opening_pt = _opening_surface_center(target, target_p)
arm_corners = obb_corners(arm_p.x, arm_p.y, arm.bbox[0], arm.bbox[1], arm_p.theta)
nearest = nearest_point_on_obb(opening_pt[0], opening_pt[1], arm_corners)
cost = _crossing_penalty(
opening_pt, nearest,
"arm", "target",
device_map, placement_map,
)
# blocker 宽 0.5mtheta=0路径水平 → 穿过长度 ≈ 0.5m
# cost = DEFAULT_WEIGHT_DISTANCE * 0.5 = 100 * 0.5 = 50
assert cost > 0
assert abs(cost - DEFAULT_WEIGHT_DISTANCE * 0.5) < DEFAULT_WEIGHT_DISTANCE * 0.1
def test_blocker_off_path_returns_zero(self):
"""不在路径上的设备 → 交叉代价为 0。"""
arm = self._make_device("arm", bbox=(2.14, 0.35))
target = self._make_device("target")
bystander = self._make_device("bystander", bbox=(0.5, 0.5))
arm_p = Placement(device_id="arm", x=3.0, y=1.0, theta=0.0)
target_p = Placement(device_id="target", x=0.0, y=1.0, theta=0.0)
bystander_p = Placement(device_id="bystander", x=1.5, y=3.0, theta=0.0)
device_map = {"arm": arm, "target": target, "bystander": bystander}
placement_map = {"arm": arm_p, "target": target_p, "bystander": bystander_p}
opening_pt = _opening_surface_center(target, target_p)
arm_corners = obb_corners(arm_p.x, arm_p.y, arm.bbox[0], arm.bbox[1], arm_p.theta)
nearest = nearest_point_on_obb(opening_pt[0], opening_pt[1], arm_corners)
cost = _crossing_penalty(
opening_pt, nearest,
"arm", "target",
device_map, placement_map,
)
assert cost == 0.0

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@@ -1,234 +0,0 @@
"""device_catalog 双源加载测试。"""
from __future__ import annotations
from pathlib import Path
import pytest
from ..device_catalog import (
_DEFAULT_FOOTPRINTS,
create_devices_from_list,
load_devices_from_assets,
load_devices_from_registry,
load_footprints,
merge_device_lists,
reset_footprints_cache,
resolve_device,
)
# ---------- fixtures ----------
# LeapLab/layout_optimizer/tests/ → LeapLab/ → DPTech/
_LEAPLAB = Path(__file__).resolve().parent.parent.parent
_DPTECH = _LEAPLAB.parent
DATA_JSON = _DPTECH / "uni-lab-assets" / "data.json"
REGISTRY_DIR = _LEAPLAB / "Uni-Lab-OS" / "unilabos" / "device_mesh" / "devices"
@pytest.fixture(autouse=True)
def _clear_cache():
"""每个测试前清除缓存。"""
reset_footprints_cache()
yield
reset_footprints_cache()
# ---------- footprints ----------
class TestLoadFootprints:
def test_load_footprints_exists(self):
fp = load_footprints(_DEFAULT_FOOTPRINTS)
assert isinstance(fp, dict)
assert len(fp) > 0
def test_footprint_structure(self):
fp = load_footprints()
for dev_id, entry in fp.items():
assert "bbox" in entry, f"{dev_id} missing bbox"
assert len(entry["bbox"]) == 2
assert "height" in entry
assert "origin_offset" in entry
assert "openings" in entry
def test_known_device_in_footprints(self):
fp = load_footprints()
assert "agilent_bravo" in fp
bbox = fp["agilent_bravo"]["bbox"]
assert 0.5 < bbox[0] < 1.0 # width ~0.65m
assert 0.5 < bbox[1] < 1.0 # depth ~0.70m
def test_nonexistent_path_returns_empty(self):
reset_footprints_cache()
fp = load_footprints("/nonexistent/footprints.json")
assert fp == {}
# ---------- assets 加载 ----------
class TestLoadFromAssets:
@pytest.mark.skipif(not DATA_JSON.exists(), reason="data.json not found")
def test_load_returns_devices(self):
devices = load_devices_from_assets(DATA_JSON)
assert len(devices) > 0
@pytest.mark.skipif(not DATA_JSON.exists(), reason="data.json not found")
def test_known_device_has_real_bbox(self):
devices = load_devices_from_assets(DATA_JSON)
bravo = next((d for d in devices if d.id == "agilent_bravo"), None)
assert bravo is not None
assert bravo.bbox != (0.6, 0.4) # 不是默认值
assert bravo.source == "assets"
def test_missing_data_json(self):
devices = load_devices_from_assets("/nonexistent/data.json")
assert devices == []
# ---------- registry 加载 ----------
class TestLoadFromRegistry:
@pytest.mark.skipif(not REGISTRY_DIR.exists(), reason="registry dir not found")
def test_load_returns_devices(self):
devices = load_devices_from_registry(REGISTRY_DIR)
assert len(devices) > 0
@pytest.mark.skipif(not REGISTRY_DIR.exists(), reason="registry dir not found")
def test_elite_robot_present(self):
devices = load_devices_from_registry(REGISTRY_DIR)
elite = next((d for d in devices if d.id == "elite_robot"), None)
assert elite is not None
assert elite.source == "registry"
def test_missing_dir(self):
devices = load_devices_from_registry("/nonexistent/")
assert devices == []
# ---------- 合并与去重 ----------
class TestMergeDedup:
def test_registry_wins_dedup(self):
from ..models import Device
reg = [Device(id="ot2", name="OT-2 Registry", bbox=(0.62, 0.50), source="registry")]
asset = [Device(id="ot2", name="OT-2 Assets", bbox=(0.62, 0.50), source="assets")]
merged = merge_device_lists(reg, asset)
ot2 = next(d for d in merged if d.id == "ot2")
assert ot2.source == "registry"
assert ot2.name == "OT-2 Registry"
def test_merge_preserves_unique(self):
from ..models import Device
reg = [Device(id="elite", name="Elite", source="registry")]
asset = [Device(id="bravo", name="Bravo", source="assets")]
merged = merge_device_lists(reg, asset)
ids = {d.id for d in merged}
assert ids == {"elite", "bravo"}
def test_registry_inherits_asset_model(self):
from ..models import Device
reg = [Device(id="ot2", name="OT-2", source="registry", model_path="")]
asset = [Device(id="ot2", name="OT-2", source="assets", model_path="/models/ot2/mesh.glb")]
merged = merge_device_lists(reg, asset)
ot2 = next(d for d in merged if d.id == "ot2")
assert ot2.model_path == "/models/ot2/mesh.glb"
# ---------- resolve_device ----------
class TestResolveDevice:
def test_known_device(self):
dev = resolve_device("agilent_bravo")
assert dev is not None
assert dev.id == "agilent_bravo"
assert dev.bbox != (0.6, 0.4)
def test_fallback_known_sizes(self):
dev = resolve_device("ot2")
assert dev is not None
assert dev.bbox == (0.62, 0.50)
def test_unknown_device_returns_none(self):
dev = resolve_device("totally_unknown_device_xyz")
assert dev is None
# ---------- create_devices_from_list (向后兼容) ----------
class TestCreateDevicesFromList:
def test_basic(self):
specs = [{"id": "test_dev", "name": "Test"}]
devs = create_devices_from_list(specs)
assert len(devs) == 1
assert devs[0].id == "test_dev"
def test_with_explicit_size(self):
specs = [{"id": "custom", "name": "Custom", "size": [1.0, 0.5]}]
devs = create_devices_from_list(specs)
assert devs[0].bbox == (1.0, 0.5)
def test_footprint_size_used_when_no_explicit(self):
specs = [{"id": "agilent_bravo", "name": "Bravo"}]
devs = create_devices_from_list(specs)
assert devs[0].bbox != (0.6, 0.4) # 使用 footprints 中的真实尺寸
def test_duplicate_catalog_ids_use_suffixes_and_store_uuid(self):
specs = [
{"id": "opentrons_liquid_handler", "uuid": "u1"},
{"id": "opentrons_liquid_handler", "uuid": "u2"},
]
devs = create_devices_from_list(specs)
assert [dev.id for dev in devs] == [
"opentrons_liquid_handler",
"opentrons_liquid_handler#2",
]
assert [dev.uuid for dev in devs] == ["u1", "u2"]
# ---------- server endpoint (需要 httpx) ----------
class TestDevicesEndpoint:
def test_get_devices(self):
try:
from fastapi.testclient import TestClient
except ImportError:
pytest.skip("fastapi testclient not available")
from ..server import app
client = TestClient(app)
resp = client.get("/devices")
assert resp.status_code == 200
data = resp.json()
assert isinstance(data, list)
# 可能为空(取决于 uni-lab-assets 是否在预期路径)
if len(data) > 0:
first = data[0]
assert "id" in first
assert "bbox" in first
assert "source" in first
def test_filter_by_source(self):
try:
from fastapi.testclient import TestClient
except ImportError:
pytest.skip("fastapi testclient not available")
from ..server import app
client = TestClient(app)
resp = client.get("/devices?source=registry")
assert resp.status_code == 200
data = resp.json()
for d in data:
assert d["source"] == "registry"

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@@ -1,263 +0,0 @@
"""End-to-end pipeline test: intents → interpret → optimize → verify.
Tests each stage boundary independently so failures are easy to localize.
Uses real PCR workflow devices with footprints from the catalog.
"""
import math
import pytest
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
# -- Scene: 5 PCR devices the user has already placed in the scene --
PCR_DEVICES = [
{"id": "thermo_orbitor_rs2_hotel", "name": "Plate Hotel", "device_type": "static"},
{"id": "arm_slider", "name": "Robot Arm", "device_type": "articulation"},
{"id": "opentrons_liquid_handler", "name": "Liquid Handler", "device_type": "static"},
{"id": "agilent_plateloc", "name": "Plate Sealer", "device_type": "static"},
{"id": "inheco_odtc_96xl", "name": "Thermal Cycler", "device_type": "static"},
]
PCR_LAB = {"width": 6.0, "depth": 4.0}
# -- Stage 1: simulated LLM output (what the LLM would produce from NL) --
# User said: "take plate from hotel, prepare sample in opentrons,
# seal plate then pcr cycle, arm_slider handles transfers"
LLM_INTENTS = [
{
"intent": "reachable_by",
"params": {
"arm": "arm_slider",
"targets": [
"thermo_orbitor_rs2_hotel",
"opentrons_liquid_handler",
"agilent_plateloc",
"inheco_odtc_96xl",
],
},
"description": "arm_slider must reach all workflow devices",
},
{
"intent": "workflow_hint",
"params": {
"workflow": "pcr",
"devices": [
"thermo_orbitor_rs2_hotel",
"opentrons_liquid_handler",
"agilent_plateloc",
"inheco_odtc_96xl",
],
},
"description": "PCR order: hotel → liquid handler → sealer → thermal cycler",
},
{
"intent": "close_together",
"params": {
"devices": ["opentrons_liquid_handler", "agilent_plateloc"],
"priority": "high",
},
"description": "Seal immediately after sample prep",
},
{
"intent": "min_spacing",
"params": {"min_gap": 0.15},
"description": "Minimum 15cm gap for accessibility",
},
]
class TestStage1Interpret:
"""Stage 1: /interpret translates intents → constraints."""
def test_interpret_returns_correct_constraint_count(self):
resp = client.post("/interpret", json={"intents": LLM_INTENTS})
assert resp.status_code == 200
data = resp.json()
# 4 reachability + 3 workflow minimize + 1 close minimize + 1 min_spacing = 9
assert len(data["constraints"]) == 9
assert len(data["errors"]) == 0
def test_interpret_has_translations_for_each_intent(self):
resp = client.post("/interpret", json={"intents": LLM_INTENTS})
data = resp.json()
assert len(data["translations"]) == len(LLM_INTENTS)
# 每个 translation 都有 explanation
for t in data["translations"]:
assert t["explanation"] != ""
def test_interpret_extracts_workflow_edges(self):
resp = client.post("/interpret", json={"intents": LLM_INTENTS})
data = resp.json()
assert len(data["workflow_edges"]) == 3
assert ["thermo_orbitor_rs2_hotel", "opentrons_liquid_handler"] in data["workflow_edges"]
assert ["opentrons_liquid_handler", "agilent_plateloc"] in data["workflow_edges"]
assert ["agilent_plateloc", "inheco_odtc_96xl"] in data["workflow_edges"]
def test_interpret_constraint_types_correct(self):
resp = client.post("/interpret", json={"intents": LLM_INTENTS})
data = resp.json()
constraints = data["constraints"]
by_rule = {}
for c in constraints:
by_rule.setdefault(c["rule_name"], []).append(c)
assert len(by_rule["reachability"]) == 4
assert all(c["type"] == "hard" for c in by_rule["reachability"])
assert len(by_rule["minimize_distance"]) == 4 # 3 workflow + 1 close
assert all(c["type"] == "soft" for c in by_rule["minimize_distance"])
assert len(by_rule["min_spacing"]) == 1
assert by_rule["min_spacing"][0]["type"] == "hard"
class TestStage2Optimize:
"""Stage 2: pipe /interpret output into /optimize → placements."""
@pytest.fixture()
def interpret_result(self):
resp = client.post("/interpret", json={"intents": LLM_INTENTS})
return resp.json()
def test_optimize_accepts_interpret_output(self, interpret_result):
"""Constraints + workflow_edges from /interpret are valid /optimize input."""
resp = client.post("/optimize", json={
"devices": PCR_DEVICES,
"lab": PCR_LAB,
"constraints": interpret_result["constraints"],
"workflow_edges": interpret_result["workflow_edges"],
"run_de": False, # seeder only — fast
})
assert resp.status_code == 200
data = resp.json()
assert len(data["placements"]) == 5
assert data["success"] is True
def test_optimize_with_de(self, interpret_result):
"""Full DE optimization completes without error."""
resp = client.post("/optimize", json={
"devices": PCR_DEVICES,
"lab": PCR_LAB,
"constraints": interpret_result["constraints"],
"workflow_edges": interpret_result["workflow_edges"],
"run_de": True,
"maxiter": 50, # reduced for test speed
"seed": 42,
})
assert resp.status_code == 200
data = resp.json()
assert len(data["placements"]) == 5
assert data["de_ran"] is True
class TestStage3VerifyPlacements:
"""Stage 3: verify optimized placements satisfy constraint intent."""
@pytest.fixture()
def placements(self):
# Full pipeline: interpret → optimize (with DE), all intents including reachability
# MockReachabilityChecker uses large fallback reach for unknown arms like arm_slider
interpret_resp = client.post("/interpret", json={"intents": LLM_INTENTS})
interpret_data = interpret_resp.json()
optimize_resp = client.post("/optimize", json={
"devices": PCR_DEVICES,
"lab": PCR_LAB,
"constraints": interpret_data["constraints"],
"workflow_edges": interpret_data["workflow_edges"],
"run_de": True,
"maxiter": 50,
"seed": 42,
})
return {p["device_id"]: p for p in optimize_resp.json()["placements"]}
def test_all_devices_placed(self, placements):
expected_ids = {d["id"] for d in PCR_DEVICES}
assert set(placements.keys()) == expected_ids
def test_all_within_lab_bounds(self, placements):
for dev_id, p in placements.items():
assert 0 <= p["position"]["x"] <= PCR_LAB["width"], f"{dev_id} x out of bounds"
assert 0 <= p["position"]["y"] <= PCR_LAB["depth"], f"{dev_id} y out of bounds"
def test_no_hard_constraint_violation(self):
"""Full pipeline with all intents including reachability converges cleanly.
MockReachabilityChecker now includes arm_slider in the default reach table
(1.07m). Binary final evaluation checks all hard constraints including
user-defined reachability.
"""
interpret_data = client.post("/interpret", json={"intents": LLM_INTENTS}).json()
optimize_resp = client.post("/optimize", json={
"devices": PCR_DEVICES,
"lab": PCR_LAB,
"constraints": interpret_data["constraints"],
"workflow_edges": interpret_data["workflow_edges"],
"run_de": True,
"maxiter": 100,
"seed": 42,
"snap_cardinal": True,
"seeder_overrides": {"align_weight": 60},
})
data = optimize_resp.json()
assert data["success"] is True
assert not math.isinf(data["cost"])
def test_workflow_neighbors_closer_than_diagonal(self, placements):
"""Workflow-adjacent devices should be closer than lab diagonal (basic sanity)."""
max_diagonal = math.sqrt(PCR_LAB["width"] ** 2 + PCR_LAB["depth"] ** 2)
workflow_pairs = [
("thermo_orbitor_rs2_hotel", "opentrons_liquid_handler"),
("opentrons_liquid_handler", "agilent_plateloc"),
("agilent_plateloc", "inheco_odtc_96xl"),
]
for a_id, b_id in workflow_pairs:
a, b = placements[a_id], placements[b_id]
dist = math.sqrt(
(a["position"]["x"] - b["position"]["x"]) ** 2
+ (a["position"]["y"] - b["position"]["y"]) ** 2
)
# 应该远小于对角线workflow minimize_distance 约束)
assert dist < max_diagonal * 0.8, (
f"Workflow pair {a_id}{b_id} distance {dist:.2f}m "
f"exceeds 80% of diagonal {max_diagonal:.2f}m"
)
class TestPipelineStageIsolation:
"""Verify each stage's output format is valid input for the next stage."""
def test_interpret_output_schema_matches_optimize_input(self):
"""constraints from /interpret have all fields /optimize expects."""
resp = client.post("/interpret", json={"intents": LLM_INTENTS})
data = resp.json()
for c in data["constraints"]:
assert "type" in c
assert "rule_name" in c
assert "params" in c
assert "weight" in c
assert c["type"] in ("hard", "soft")
for edge in data["workflow_edges"]:
assert isinstance(edge, list)
assert len(edge) == 2
def test_round_trip_no_data_loss(self):
"""Interpret → optimize → check that all device IDs survive the pipeline."""
interpret_resp = client.post("/interpret", json={"intents": LLM_INTENTS})
interpret_data = interpret_resp.json()
optimize_resp = client.post("/optimize", json={
"devices": PCR_DEVICES,
"lab": PCR_LAB,
"constraints": interpret_data["constraints"],
"workflow_edges": interpret_data["workflow_edges"],
"run_de": False,
})
result_ids = {p["device_id"] for p in optimize_resp.json()["placements"]}
input_ids = {d["id"] for d in PCR_DEVICES}
assert result_ids == input_ids

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@@ -1,210 +0,0 @@
"""Intent interpreter tests — PCR workflow devices."""
import pytest
from ..intent_interpreter import interpret_intents
from ..models import Intent
# --- reachable_by ---
def test_reachable_by_generates_hard_reachability():
intents = [Intent(
intent="reachable_by",
params={"arm": "arm_slider", "targets": ["opentrons_liquid_handler", "inheco_odtc_96xl"]},
description="Robot arm must reach liquid handler and thermal cycler",
)]
result = interpret_intents(intents)
assert len(result.constraints) == 2
assert all(c.rule_name == "reachability" for c in result.constraints)
assert all(c.type == "hard" for c in result.constraints)
assert result.constraints[0].params == {"arm_id": "arm_slider", "target_device_id": "opentrons_liquid_handler"}
assert result.constraints[1].params == {"arm_id": "arm_slider", "target_device_id": "inheco_odtc_96xl"}
assert len(result.translations) == 1
assert len(result.translations[0]["generated_constraints"]) == 2
def test_reachable_by_missing_arm():
result = interpret_intents([Intent(intent="reachable_by", params={"targets": ["a"]})])
assert len(result.constraints) == 0
assert len(result.errors) == 1
assert "arm" in result.errors[0].lower()
def test_reachable_by_empty_targets():
result = interpret_intents([Intent(intent="reachable_by", params={"arm": "arm_slider", "targets": []})])
assert len(result.constraints) == 0
assert len(result.errors) == 1
assert "targets" in result.errors[0].lower()
# --- close_together ---
def test_close_together_generates_minimize_distance():
intents = [Intent(intent="close_together", params={
"devices": ["opentrons_liquid_handler", "inheco_odtc_96xl", "agilent_plateloc"],
})]
result = interpret_intents(intents)
assert len(result.constraints) == 3 # C(3,2) = 3 pairs
assert all(c.rule_name == "minimize_distance" for c in result.constraints)
assert all(c.type == "soft" for c in result.constraints)
def test_close_together_priority_scales_weight():
low = interpret_intents([Intent(intent="close_together", params={"devices": ["a", "b"], "priority": "low"})])
high = interpret_intents([Intent(intent="close_together", params={"devices": ["a", "b"], "priority": "high"})])
assert high.constraints[0].weight > low.constraints[0].weight
assert high.constraints[0].weight == pytest.approx(16.0)
assert low.constraints[0].weight == pytest.approx(0.5)
def test_close_together_single_device_error():
result = interpret_intents([Intent(intent="close_together", params={"devices": ["a"]})])
assert len(result.errors) == 1
# --- far_apart ---
def test_far_apart_generates_maximize_distance():
result = interpret_intents([Intent(intent="far_apart", params={
"devices": ["inheco_odtc_96xl", "thermo_orbitor_rs2_hotel"],
})])
assert len(result.constraints) == 1
assert result.constraints[0].rule_name == "maximize_distance"
# --- max_distance / min_distance ---
def test_max_distance_generates_distance_less_than():
result = interpret_intents([Intent(intent="max_distance", params={
"device_a": "opentrons_liquid_handler", "device_b": "inheco_odtc_96xl", "distance": 1.5,
})])
assert len(result.constraints) == 1
c = result.constraints[0]
assert c.rule_name == "distance_less_than"
assert c.type == "hard"
assert c.params["distance"] == 1.5
def test_min_distance_generates_distance_greater_than():
result = interpret_intents([Intent(intent="min_distance", params={
"device_a": "inheco_odtc_96xl", "device_b": "thermo_orbitor_rs2_hotel", "distance": 2.0,
})])
c = result.constraints[0]
assert c.rule_name == "distance_greater_than"
assert c.type == "hard"
assert c.params["distance"] == 2.0
def test_max_distance_zero_is_valid():
"""distance=0 is falsy but valid — must not be rejected."""
result = interpret_intents([Intent(intent="max_distance", params={
"device_a": "a", "device_b": "b", "distance": 0,
})])
assert len(result.constraints) == 1
assert len(result.errors) == 0
def test_max_distance_missing_param():
result = interpret_intents([Intent(intent="max_distance", params={"device_a": "a"})])
assert len(result.errors) == 1
assert len(result.constraints) == 0
# --- orientation ---
def test_face_outward():
result = interpret_intents([Intent(intent="face_outward")])
assert result.constraints[0].rule_name == "prefer_orientation_mode"
assert result.constraints[0].params["mode"] == "outward"
def test_face_inward():
result = interpret_intents([Intent(intent="face_inward")])
assert result.constraints[0].params["mode"] == "inward"
def test_align_cardinal():
result = interpret_intents([Intent(intent="align_cardinal")])
assert result.constraints[0].rule_name == "prefer_aligned"
assert result.constraints[0].weight == pytest.approx(0.5)
def test_keep_adjacent_generates_minimize_distance():
result = interpret_intents([Intent(intent="keep_adjacent", params={
"devices": ["opentrons_liquid_handler", "agilent_plateloc"],
"priority": "high",
})])
assert len(result.constraints) == 1
assert result.constraints[0].rule_name == "minimize_distance"
assert result.constraints[0].weight == pytest.approx(16.0)
# --- min_spacing ---
def test_min_spacing():
result = interpret_intents([Intent(intent="min_spacing", params={"min_gap": 0.3})])
c = result.constraints[0]
assert c.rule_name == "min_spacing"
assert c.type == "hard"
assert c.params["min_gap"] == 0.3
# --- workflow_hint (PCR scenario) ---
def test_workflow_hint_pcr():
"""PCR workflow: pipette → thermal cycler → plate sealer → storage."""
intents = [Intent(
intent="workflow_hint",
params={
"workflow": "pcr",
"devices": [
"opentrons_liquid_handler",
"inheco_odtc_96xl",
"agilent_plateloc",
"thermo_orbitor_rs2_hotel",
],
},
)]
result = interpret_intents(intents)
assert len(result.constraints) == 3 # 4 devices → 3 consecutive pairs
assert all(c.rule_name == "minimize_distance" for c in result.constraints)
assert len(result.workflow_edges) == 3
assert ["opentrons_liquid_handler", "inheco_odtc_96xl"] in result.workflow_edges
assert result.translations[0]["confidence"] == "low"
def test_workflow_hint_single_device_error():
result = interpret_intents([Intent(intent="workflow_hint", params={"workflow": "test", "devices": ["a"]})])
assert len(result.errors) == 1
# --- unknown intent ---
def test_unknown_intent():
result = interpret_intents([Intent(intent="nonexistent")])
assert len(result.constraints) == 0
assert len(result.errors) == 1
assert "nonexistent" in result.errors[0]
# --- multi-intent combination ---
def test_full_pcr_scenario():
"""Arm reachability + close together for full PCR setup."""
intents = [
Intent(intent="reachable_by", params={
"arm": "arm_slider",
"targets": [
"opentrons_liquid_handler", "inheco_odtc_96xl",
"agilent_plateloc", "thermo_orbitor_rs2_hotel",
],
}),
Intent(intent="close_together", params={
"devices": ["opentrons_liquid_handler", "inheco_odtc_96xl"],
"priority": "high",
}),
]
result = interpret_intents(intents)
assert len(result.constraints) == 5 # 4 reachability + 1 minimize_distance
assert len(result.translations) == 2
assert len(result.errors) == 0

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@@ -1,134 +0,0 @@
"""Tests for /interpret and /interpret/schema API endpoints."""
import pytest
from fastapi.testclient import TestClient
from ..server import app
client = TestClient(app)
def test_interpret_reachable_by():
resp = client.post("/interpret", json={
"intents": [
{
"intent": "reachable_by",
"params": {
"arm": "arm_slider",
"targets": ["opentrons_liquid_handler", "inheco_odtc_96xl"],
},
"description": "Arm must reach liquid handler and thermal cycler",
}
]
})
assert resp.status_code == 200
data = resp.json()
assert len(data["constraints"]) == 2
assert all(c["rule_name"] == "reachability" for c in data["constraints"])
assert len(data["translations"]) == 1
assert data["translations"][0]["source_intent"] == "reachable_by"
assert len(data["errors"]) == 0
def test_interpret_pcr_workflow():
"""Full PCR: reachability + workflow_hint + close_together."""
resp = client.post("/interpret", json={
"intents": [
{
"intent": "reachable_by",
"params": {
"arm": "arm_slider",
"targets": [
"opentrons_liquid_handler",
"inheco_odtc_96xl",
"agilent_plateloc",
"thermo_orbitor_rs2_hotel",
],
},
},
{
"intent": "workflow_hint",
"params": {
"workflow": "pcr",
"devices": [
"opentrons_liquid_handler",
"inheco_odtc_96xl",
"agilent_plateloc",
"thermo_orbitor_rs2_hotel",
],
},
},
{
"intent": "close_together",
"params": {
"devices": ["opentrons_liquid_handler", "inheco_odtc_96xl"],
"priority": "high",
},
},
]
})
assert resp.status_code == 200
data = resp.json()
# 4 reachability + 3 workflow + 1 close = 8
assert len(data["constraints"]) == 8
assert len(data["workflow_edges"]) == 3
assert len(data["translations"]) == 3
assert len(data["errors"]) == 0
def test_interpret_returns_errors_for_bad_intents():
resp = client.post("/interpret", json={
"intents": [
{"intent": "reachable_by", "params": {}},
{"intent": "nonexistent_intent"},
]
})
assert resp.status_code == 200
data = resp.json()
assert len(data["errors"]) == 2
assert len(data["constraints"]) == 0
def test_interpret_empty_intents():
resp = client.post("/interpret", json={"intents": []})
assert resp.status_code == 200
data = resp.json()
assert data["constraints"] == []
assert data["translations"] == []
assert data["errors"] == []
def test_interpret_schema_returns_all_intents():
resp = client.get("/interpret/schema")
assert resp.status_code == 200
data = resp.json()
intents = data["intents"]
expected = {
"reachable_by", "close_together", "far_apart", "keep_adjacent",
"max_distance", "min_distance", "min_spacing",
"workflow_hint", "face_outward", "face_inward", "align_cardinal",
}
assert set(intents.keys()) == expected
def test_interpret_constraints_passable_to_optimize():
"""Constraints from /interpret should be directly usable in /optimize."""
# Step 1: interpret
interpret_resp = client.post("/interpret", json={
"intents": [
{"intent": "close_together", "params": {"devices": ["dev_a", "dev_b"]}},
]
})
constraints = interpret_resp.json()["constraints"]
# Step 2: pass to optimize (verify it accepts the format)
optimize_resp = client.post("/optimize", json={
"devices": [
{"id": "dev_a", "name": "Device A", "size": [0.5, 0.4]},
{"id": "dev_b", "name": "Device B", "size": [0.5, 0.4]},
],
"lab": {"width": 4.0, "depth": 3.0},
"constraints": constraints,
"run_de": False,
})
assert optimize_resp.status_code == 200
assert len(optimize_resp.json()["placements"]) == 2

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@@ -1,277 +0,0 @@
"""LLM 技能文档测试:用真实 LLM 验证模糊用户输入 → 结构化意图的翻译质量。
需要 ANTHROPIC_API_KEY 环境变量。无 key 时自动跳过。
测试覆盖设备名模糊匹配、工作流顺序推理、约束类型选择、JSON 格式正确性。
"""
import json
import os
import pytest
HAS_API_KEY = bool(os.environ.get("ANTHROPIC_API_KEY"))
pytestmark = pytest.mark.skipif(not HAS_API_KEY, reason="ANTHROPIC_API_KEY not set")
# 读取技能文档
_SKILL_DOC_PATH = os.path.join(
os.path.dirname(__file__), "..", "llm_skill", "layout_intent_translator.md"
)
# PCR 场景设备列表(模拟用户场景中已有的设备)
SCENE_DEVICE_LIST = """\
Devices in scene:
- thermo_orbitor_rs2_hotel: Thermo Orbitor RS2 Hotel (type: static, bbox: 0.68×0.52m)
- arm_slider: Arm Slider (type: articulation, bbox: 1.20×0.30m)
- opentrons_liquid_handler: Opentrons Liquid Handler (type: static, bbox: 0.65×0.60m)
- agilent_plateloc: Agilent PlateLoc (type: static, bbox: 0.35×0.40m)
- inheco_odtc_96xl: Inheco ODTC 96XL (type: static, bbox: 0.30×0.35m)
"""
VALID_DEVICE_IDS = {
"thermo_orbitor_rs2_hotel",
"arm_slider",
"opentrons_liquid_handler",
"agilent_plateloc",
"inheco_odtc_96xl",
}
VALID_INTENT_TYPES = {
"reachable_by", "close_together", "far_apart", "max_distance",
"min_distance", "min_spacing", "workflow_hint",
"face_outward", "face_inward", "align_cardinal",
}
def _call_llm(user_message: str) -> dict:
"""调用 LLM使用技能文档作为 system prompt返回解析后的 JSON。"""
import anthropic
with open(_SKILL_DOC_PATH) as f:
skill_doc = f.read()
client = anthropic.Anthropic()
response = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=2000,
system=skill_doc,
messages=[
{"role": "user", "content": f"{SCENE_DEVICE_LIST}\n\n{user_message}"},
],
)
# 从 response 中提取 JSON
text = response.content[0].text
# LLM 可能返回 ```json ... ``` 包裹的 JSON
if "```json" in text:
text = text.split("```json")[1].split("```")[0]
elif "```" in text:
text = text.split("```")[1].split("```")[0]
return json.loads(text.strip())
def _extract_all_device_ids(intents: list[dict]) -> set[str]:
"""从意图列表中提取所有引用的设备 ID。"""
ids = set()
for intent in intents:
params = intent.get("params", {})
if "arm" in params:
ids.add(params["arm"])
for key in ("targets", "devices"):
if key in params:
ids.update(params[key])
for key in ("device_a", "device_b"):
if key in params:
ids.add(params[key])
return ids
class TestLLMFuzzyDeviceResolution:
"""测试 LLM 能否将模糊设备名映射到精确 ID。"""
def test_pcr_machine_resolves_to_inheco(self):
"""'PCR machine' 应解析为 inheco_odtc_96xl。"""
result = _call_llm(
"Keep the PCR machine close to the plate sealer"
)
intents = result["intents"]
all_ids = _extract_all_device_ids(intents)
assert "inheco_odtc_96xl" in all_ids, f"Expected inheco_odtc_96xl in {all_ids}"
assert "agilent_plateloc" in all_ids, f"Expected agilent_plateloc in {all_ids}"
def test_robot_resolves_to_articulation_type(self):
"""'the robot' / 'robot arm' 应解析为 arm_slider唯一 articulation 类型)。"""
result = _call_llm(
"The robot should be able to reach the liquid handler and the storage hotel"
)
intents = result["intents"]
all_ids = _extract_all_device_ids(intents)
assert "arm_slider" in all_ids, f"Expected arm_slider in {all_ids}"
assert "opentrons_liquid_handler" in all_ids
assert "thermo_orbitor_rs2_hotel" in all_ids
def test_all_resolved_ids_are_valid(self):
"""LLM 输出的所有设备 ID 必须来自场景设备列表。"""
result = _call_llm(
"Take plate from hotel, prepare sample in the pipetting robot, "
"seal it, then run thermal cycling. The arm handles all transfers."
)
intents = result["intents"]
all_ids = _extract_all_device_ids(intents)
invalid = all_ids - VALID_DEVICE_IDS
assert not invalid, f"LLM produced invalid device IDs: {invalid}"
class TestLLMWorkflowInterpretation:
"""测试 LLM 对工作流描述的理解和翻译。"""
def test_pcr_workflow_full(self):
"""完整 PCR 工作流描述应生成 reachable_by + workflow_hint + close_together。"""
result = _call_llm(
"I need to set up a PCR workflow: take plate from the hotel, "
"prepare the sample in the liquid handler, seal the plate, "
"then run the thermal cycler. The robot arm handles all plate transfers. "
"Keep the liquid handler and sealer close together."
)
intents = result["intents"]
intent_types = {i["intent"] for i in intents}
# 应包含核心意图类型
assert "reachable_by" in intent_types, f"Missing reachable_by in {intent_types}"
assert "workflow_hint" in intent_types, f"Missing workflow_hint in {intent_types}"
# reachable_by 应包含所有工作流设备作为 targets
reach_intents = [i for i in intents if i["intent"] == "reachable_by"]
assert len(reach_intents) >= 1
reach_targets = set()
for ri in reach_intents:
reach_targets.update(ri["params"].get("targets", []))
# 至少液体处理器和热循环仪应在可达范围内
assert "opentrons_liquid_handler" in reach_targets
assert "inheco_odtc_96xl" in reach_targets
def test_workflow_device_order(self):
"""workflow_hint 的设备顺序应反映工作流步骤。"""
result = _call_llm(
"PCR process: first the hotel dispenses a plate, then the opentrons "
"prepares the sample, next the plateloc seals it, finally the thermal "
"cycler runs PCR. Generate a workflow hint."
)
intents = result["intents"]
wf_intents = [i for i in intents if i["intent"] == "workflow_hint"]
assert len(wf_intents) >= 1, f"No workflow_hint found in {[i['intent'] for i in intents]}"
devices = wf_intents[0]["params"]["devices"]
# 验证顺序hotel → liquid_handler → plateloc → thermal_cycler
hotel_idx = devices.index("thermo_orbitor_rs2_hotel")
lh_idx = devices.index("opentrons_liquid_handler")
seal_idx = devices.index("agilent_plateloc")
tc_idx = devices.index("inheco_odtc_96xl")
assert hotel_idx < lh_idx < seal_idx < tc_idx, (
f"Wrong workflow order: {devices}"
)
class TestLLMOutputFormat:
"""测试 LLM 输出格式的正确性。"""
def test_output_has_intents_array(self):
"""输出必须有 intents 数组。"""
result = _call_llm("Keep all devices at least 30cm apart")
assert "intents" in result
assert isinstance(result["intents"], list)
assert len(result["intents"]) > 0
def test_each_intent_has_required_fields(self):
"""每个意图必须有 intent、params、description。"""
result = _call_llm(
"The robot arm should reach the liquid handler. "
"Keep the thermal cycler away from the plate hotel."
)
for intent in result["intents"]:
assert "intent" in intent, f"Missing 'intent' field: {intent}"
assert "params" in intent, f"Missing 'params' field: {intent}"
assert "description" in intent, f"Missing 'description' field: {intent}"
def test_intent_types_are_valid(self):
"""所有意图类型必须是已知类型。"""
result = _call_llm(
"Set up a compact PCR line: hotel → liquid handler → sealer → thermal cycler. "
"Robot arm handles transfers. Align everything neatly."
)
for intent in result["intents"]:
assert intent["intent"] in VALID_INTENT_TYPES, (
f"Unknown intent type: {intent['intent']}"
)
class TestLLMInterpretThenOptimize:
"""端到端LLM 翻译 → /interpret → /optimize → 验证布局。"""
def test_llm_output_accepted_by_interpret_endpoint(self):
"""LLM 输出应能直接被 /interpret 端点接受。"""
from fastapi.testclient import TestClient
from ..server import app
test_client = TestClient(app)
llm_result = _call_llm(
"Take plate from hotel, prepare sample in opentrons, "
"seal plate then pcr cycle, arm_slider handles all transfers. "
"Keep liquid handler and sealer close."
)
# /interpret 应接受 LLM 输出
resp = test_client.post("/interpret", json=llm_result)
assert resp.status_code == 200, f"Interpret failed: {resp.text}"
data = resp.json()
assert len(data["constraints"]) > 0, "No constraints generated"
assert len(data["errors"]) == 0, f"Interpretation errors: {data['errors']}"
def test_full_pipeline_llm_to_placement(self):
"""LLM 翻译 → interpret → optimize → 所有设备有 placement。"""
from fastapi.testclient import TestClient
from ..server import app
test_client = TestClient(app)
# Stage 1: LLM 翻译
llm_result = _call_llm(
"I want a PCR workflow lab. Take plate from the hotel, pipette in the "
"liquid handler, seal with the plateloc, then thermal cycle. "
"The robot arm does all transfers between devices. "
"Minimum 15cm gap between everything."
)
# Stage 2: interpret
interpret_resp = test_client.post("/interpret", json=llm_result)
assert interpret_resp.status_code == 200
interpret_data = interpret_resp.json()
assert len(interpret_data["errors"]) == 0
# Stage 3: optimize
pcr_devices = [
{"id": "thermo_orbitor_rs2_hotel", "name": "Plate Hotel", "device_type": "static"},
{"id": "arm_slider", "name": "Robot Arm", "device_type": "articulation"},
{"id": "opentrons_liquid_handler", "name": "Liquid Handler", "device_type": "static"},
{"id": "agilent_plateloc", "name": "Plate Sealer", "device_type": "static"},
{"id": "inheco_odtc_96xl", "name": "Thermal Cycler", "device_type": "static"},
]
optimize_resp = test_client.post("/optimize", json={
"devices": pcr_devices,
"lab": {"width": 6.0, "depth": 4.0},
"constraints": interpret_data["constraints"],
"workflow_edges": interpret_data.get("workflow_edges", []),
"run_de": True,
"maxiter": 50,
"seed": 42,
})
assert optimize_resp.status_code == 200
data = optimize_resp.json()
# Stage 4: 验证所有设备都有 placement
placed_ids = {p["device_id"] for p in data["placements"]}
expected_ids = {d["id"] for d in pcr_devices}
assert placed_ids == expected_ids
assert data["success"] is True

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@@ -1,138 +0,0 @@
"""MockCollisionChecker 和 MockReachabilityChecker 测试。"""
import math
from ..mock_checkers import MockCollisionChecker, MockReachabilityChecker
class TestMockCollisionChecker:
def setup_method(self):
self.checker = MockCollisionChecker()
def test_no_collision_far_apart(self):
"""两个设备距离足够远,不碰撞。"""
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (0.5, 0.5), "pos": (3.0, 3.0, 0.0)},
]
assert self.checker.check(placements) == []
def test_collision_overlapping(self):
"""两个设备重叠,应检测到碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (1.0, 1.0), "pos": (1.5, 1.0, 0.0)},
]
collisions = self.checker.check(placements)
assert ("a", "b") in collisions
def test_collision_touching_edges(self):
"""两设备恰好边缘接触,不算碰撞(< 而非 <=)。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (0.5, 0.5, 0.0)},
{"id": "b", "bbox": (1.0, 1.0), "pos": (1.5, 0.5, 0.0)},
]
collisions = self.checker.check(placements)
assert collisions == []
def test_collision_with_rotation(self):
"""旋转后的设备 OBB 可能导致碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 0.2), "pos": (1.0, 1.0, math.pi / 4)},
{"id": "b", "bbox": (0.5, 0.5), "pos": (1.4, 1.0, 0.0)}, # closer: OBB overlap
]
collisions = self.checker.check(placements)
assert ("a", "b") in collisions
def test_no_collision_with_rotation(self):
"""旋转后仍不碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 0.2), "pos": (1.0, 1.0, math.pi / 4)},
{"id": "b", "bbox": (0.5, 0.5), "pos": (2.0, 1.0, 0.0)},
]
collisions = self.checker.check(placements)
assert collisions == []
def test_check_bounds_within(self):
"""设备在边界内。"""
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
]
assert self.checker.check_bounds(placements, 5.0, 5.0) == []
def test_check_bounds_outside(self):
"""设备超出边界。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (0.2, 0.2, 0.0)},
]
oob = self.checker.check_bounds(placements, 5.0, 5.0)
assert "a" in oob
def test_three_devices_multiple_collisions(self):
"""三个设备,两两碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (1.0, 1.0), "pos": (1.3, 1.0, 0.0)},
{"id": "c", "bbox": (1.0, 1.0), "pos": (1.6, 1.0, 0.0)},
]
collisions = self.checker.check(placements)
assert ("a", "b") in collisions
assert ("b", "c") in collisions
def test_obb_collision_rotated_no_false_positive():
"""A rotated narrow device should NOT collide with a nearby device
that the old AABB method would have flagged as colliding.
Old AABB expands footprint; OBB is precise.
"""
checker = MockCollisionChecker()
# Narrow device (2.0 x 0.5) rotated 45°:
# AABB would be ~1.77 x 1.77, OBB is the actual narrow rectangle
placements = [
{"id": "narrow", "bbox": (2.0, 0.5), "pos": (3.0, 3.0, math.pi / 4)},
{"id": "nearby", "bbox": (0.5, 0.5), "pos": (4.5, 3.0, 0.0)},
]
collisions = checker.check(placements)
# With OBB: no collision (the narrow rotated box doesn't reach)
assert ("narrow", "nearby") not in collisions and ("nearby", "narrow") not in collisions
class TestMockReachabilityChecker:
def setup_method(self):
self.checker = MockReachabilityChecker()
def test_reachable_within_radius(self):
"""目标在臂展半径内。"""
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 0.5, "y": 0.5, "z": 0.0}
assert self.checker.is_reachable("elite_cs66", arm_pose, target)
def test_not_reachable_outside_radius(self):
"""目标超出臂展半径。"""
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 2.0, "y": 2.0, "z": 0.0}
assert not self.checker.is_reachable("elite_cs66", arm_pose, target)
def test_reachable_at_boundary(self):
"""目标恰好在臂展边界上(应可达)。"""
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 0.914, "y": 0.0, "z": 0.0}
assert self.checker.is_reachable("elite_cs66", arm_pose, target)
def test_unknown_arm_uses_default(self):
"""未知型号使用 1.0m 回退臂展realistic lab-scale default"""
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
# Within 1.0m fallback reach
target_near = {"x": 0.8, "y": 0.0, "z": 0.0}
assert self.checker.is_reachable("unknown_arm", arm_pose, target_near)
# Beyond 1.0m fallback reach
target_far = {"x": 1.5, "y": 0.0, "z": 0.0}
assert not self.checker.is_reachable("unknown_arm", arm_pose, target_far)
def test_custom_arm_reach(self):
"""自定义臂展参数。"""
checker = MockReachabilityChecker(arm_reach={"custom_arm": 1.5})
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 1.4, "y": 0.0, "z": 0.0}
assert checker.is_reachable("custom_arm", arm_pose, target)

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@@ -1,156 +0,0 @@
"""Tests for OBB (Oriented Bounding Box) geometry utilities."""
import math
import pytest
from ..obb import obb_corners, obb_overlap, obb_min_distance, segment_obb_intersection_length
class TestObbCorners:
"""obb_corners(cx, cy, w, h, theta) → 4 corner points of the rotated rectangle."""
def test_no_rotation(self):
"""Axis-aligned box at origin: corners at ±half extents."""
corners = obb_corners(0, 0, 2.0, 1.0, 0.0)
assert len(corners) == 4
xs = sorted(c[0] for c in corners)
ys = sorted(c[1] for c in corners)
assert xs == pytest.approx([-1.0, -1.0, 1.0, 1.0])
assert ys == pytest.approx([-0.5, -0.5, 0.5, 0.5])
def test_90_degree_rotation(self):
"""90° rotation swaps width and height extents."""
corners = obb_corners(0, 0, 2.0, 1.0, math.pi / 2)
xs = sorted(c[0] for c in corners)
ys = sorted(c[1] for c in corners)
assert xs == pytest.approx([-0.5, -0.5, 0.5, 0.5])
assert ys == pytest.approx([-1.0, -1.0, 1.0, 1.0])
def test_offset_center(self):
"""Corners shift by (cx, cy)."""
corners = obb_corners(3.0, 2.0, 2.0, 1.0, 0.0)
xs = sorted(c[0] for c in corners)
ys = sorted(c[1] for c in corners)
assert xs == pytest.approx([2.0, 2.0, 4.0, 4.0])
assert ys == pytest.approx([1.5, 1.5, 2.5, 2.5])
def test_45_degree_rotation(self):
"""45° rotation: corners on diagonals."""
corners = obb_corners(0, 0, 2.0, 2.0, math.pi / 4)
for cx, cy in corners:
dist = math.sqrt(cx**2 + cy**2)
assert dist == pytest.approx(math.sqrt(2), abs=1e-9)
class TestObbOverlap:
"""obb_overlap(corners_a, corners_b) → True if the two OBBs overlap."""
def test_separated_boxes(self):
"""Two boxes far apart: no overlap."""
a = obb_corners(0, 0, 1.0, 1.0, 0.0)
b = obb_corners(5, 0, 1.0, 1.0, 0.0)
assert obb_overlap(a, b) is False
def test_overlapping_boxes(self):
"""Two boxes sharing space: overlap."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0)
b = obb_corners(1, 0, 2.0, 2.0, 0.0)
assert obb_overlap(a, b) is True
def test_touching_edges_no_overlap(self):
"""Boxes touching at edge: no overlap (strict <, not <=)."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0)
b = obb_corners(2.0, 0, 2.0, 2.0, 0.0)
assert obb_overlap(a, b) is False
def test_rotated_overlap(self):
"""One box rotated 45°, overlapping the other."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0)
b = obb_corners(1.0, 1.0, 2.0, 2.0, math.pi / 4)
assert obb_overlap(a, b) is True
def test_rotated_no_overlap(self):
"""One box rotated 45°, separated from the other."""
a = obb_corners(0, 0, 1.0, 1.0, 0.0)
b = obb_corners(3, 0, 1.0, 1.0, math.pi / 4)
assert obb_overlap(a, b) is False
def test_identical_boxes(self):
"""Same position and size: overlap."""
a = obb_corners(1, 1, 1.0, 1.0, 0.0)
b = obb_corners(1, 1, 1.0, 1.0, 0.0)
assert obb_overlap(a, b) is True
class TestObbMinDistance:
"""obb_min_distance(corners_a, corners_b) → minimum edge-to-edge distance."""
def test_overlapping_returns_zero(self):
"""Overlapping boxes: distance = 0."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0)
b = obb_corners(1, 0, 2.0, 2.0, 0.0)
assert obb_min_distance(a, b) == pytest.approx(0.0)
def test_separated_axis_aligned(self):
"""Two axis-aligned boxes with 2m gap."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0) # edges at x=±1
b = obb_corners(4, 0, 2.0, 2.0, 0.0) # edges at x=3,5
# Gap = 3 - 1 = 2.0
assert obb_min_distance(a, b) == pytest.approx(2.0)
def test_diagonal_separation(self):
"""Boxes separated diagonally: distance to nearest corner."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0) # corners at (±1, ±1)
b = obb_corners(4, 4, 2.0, 2.0, 0.0) # corners at (3..5, 3..5)
# Nearest corners: (1,1) to (3,3) → sqrt(8) ≈ 2.828
assert obb_min_distance(a, b) == pytest.approx(math.sqrt(8), abs=0.01)
def test_rotated_separation(self):
"""One rotated box separated from axis-aligned box."""
a = obb_corners(0, 0, 1.0, 1.0, 0.0)
b = obb_corners(3, 0, 1.0, 1.0, math.pi / 4)
dist = obb_min_distance(a, b)
assert dist > 0
def test_touching_returns_zero(self):
"""Touching edges: distance = 0."""
a = obb_corners(0, 0, 2.0, 2.0, 0.0)
b = obb_corners(2.0, 0, 2.0, 2.0, 0.0)
assert obb_min_distance(a, b) == pytest.approx(0.0)
class TestSegmentOBBIntersectionLength:
"""segment_obb_intersection_length: Cyrus-Beck clipping."""
def test_segment_fully_outside(self):
corners = obb_corners(0, 0, 2, 2, 0)
length = segment_obb_intersection_length((-5, 3), (5, 3), corners)
assert length == 0.0
def test_segment_fully_inside(self):
corners = obb_corners(0, 0, 4, 4, 0)
length = segment_obb_intersection_length((-0.5, 0), (0.5, 0), corners)
assert abs(length - 1.0) < 1e-6
def test_segment_crosses_through(self):
corners = obb_corners(0, 0, 2, 2, 0)
length = segment_obb_intersection_length((-5, 0), (5, 0), corners)
assert abs(length - 2.0) < 1e-6
def test_segment_partial_overlap(self):
corners = obb_corners(0, 0, 2, 2, 0)
length = segment_obb_intersection_length((0, 0), (5, 0), corners)
assert abs(length - 1.0) < 1e-6
def test_rotated_obb(self):
corners = obb_corners(0, 0, 2, 2, math.pi / 4)
length = segment_obb_intersection_length((-3, 0), (3, 0), corners)
expected = 2 * math.sqrt(2)
assert abs(length - expected) < 1e-4
def test_zero_length_segment(self):
corners = obb_corners(0, 0, 2, 2, 0)
assert segment_obb_intersection_length((0, 0), (0, 0), corners) == 0.0
def test_parallel_outside(self):
corners = obb_corners(0, 0, 2, 2, 0)
length = segment_obb_intersection_length((-5, 2), (5, 2), corners)
assert length == 0.0

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@@ -1,430 +0,0 @@
"""MoveItCollisionChecker 和 IKFastReachabilityChecker 测试。
使用 unittest.mock 模拟 MoveIt2 实例,验证适配器逻辑,
无需 ROS2 / MoveIt2 运行环境。
"""
import math
import tempfile
from pathlib import Path
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from ..ros_checkers import (
IKFastReachabilityChecker,
MoveItCollisionChecker,
_transform_to_arm_frame,
_yaw_to_quat,
_yaw_to_rotation_matrix,
create_checkers,
)
# ---------- 辅助函数测试 ----------
class TestYawToQuat:
def test_zero_rotation(self):
"""零旋转 → 单位四元数。"""
q = _yaw_to_quat(0.0)
assert q == pytest.approx((0.0, 0.0, 0.0, 1.0))
def test_90_degrees(self):
"""90° → (0, 0, sin(π/4), cos(π/4))。"""
q = _yaw_to_quat(math.pi / 2)
expected = (0.0, 0.0, math.sin(math.pi / 4), math.cos(math.pi / 4))
assert q == pytest.approx(expected)
def test_180_degrees(self):
"""180° → (0, 0, 1, 0)。"""
q = _yaw_to_quat(math.pi)
assert q == pytest.approx((0.0, 0.0, 1.0, 0.0), abs=1e-10)
class TestTransformToArmFrame:
def test_identity_transform(self):
"""臂在原点无旋转,目标在 (1, 0, 0.5)。"""
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 1.0, "y": 0.0, "z": 0.5}
local = _transform_to_arm_frame(arm_pose, target)
assert local == pytest.approx((1.0, 0.0, 0.5))
def test_translation_only(self):
"""臂在 (2, 3) 无旋转,目标在 (3, 4, 0)。"""
arm_pose = {"x": 2.0, "y": 3.0, "theta": 0.0}
target = {"x": 3.0, "y": 4.0, "z": 0.0}
local = _transform_to_arm_frame(arm_pose, target)
assert local == pytest.approx((1.0, 1.0, 0.0))
def test_rotation_90(self):
"""臂旋转 90°目标在臂前方。"""
arm_pose = {"x": 0.0, "y": 0.0, "theta": math.pi / 2}
target = {"x": 0.0, "y": 1.0, "z": 0.0}
local = _transform_to_arm_frame(arm_pose, target)
# 世界 Y+ 在臂坐标系中变成 X+
assert local[0] == pytest.approx(1.0, abs=1e-10)
assert local[1] == pytest.approx(0.0, abs=1e-10)
class TestYawToRotationMatrix:
def test_identity(self):
"""零旋转 → 单位矩阵。"""
R = _yaw_to_rotation_matrix(0.0)
np.testing.assert_allclose(R, np.eye(3), atol=1e-10)
def test_90_degrees(self):
"""90° 旋转矩阵。"""
R = _yaw_to_rotation_matrix(math.pi / 2)
expected = np.array([
[0.0, -1.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
])
np.testing.assert_allclose(R, expected, atol=1e-10)
# ---------- MoveItCollisionChecker 测试 ----------
class TestMoveItCollisionChecker:
def setup_method(self):
self.moveit2 = MagicMock()
# 禁用 FCL使用 OBB 回退(测试环境无需 python-fcl
self.checker = MoveItCollisionChecker(
self.moveit2, sync_to_scene=True,
)
self.checker._fcl_available = False
def test_no_collision_far_apart(self):
"""两个设备距离足够远,不碰撞。"""
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (0.5, 0.5), "pos": (3.0, 3.0, 0.0)},
]
assert self.checker.check(placements) == []
def test_collision_overlapping(self):
"""两个设备重叠,应检测到碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (1.0, 1.0), "pos": (1.5, 1.0, 0.0)},
]
collisions = self.checker.check(placements)
assert ("a", "b") in collisions
def test_collision_with_rotation(self):
"""旋转后的碰撞检测。"""
placements = [
{"id": "a", "bbox": (1.0, 0.2), "pos": (1.0, 1.0, math.pi / 4)},
{"id": "b", "bbox": (0.5, 0.5), "pos": (1.4, 1.0, 0.0)},
]
collisions = self.checker.check(placements)
assert ("a", "b") in collisions
def test_syncs_collision_objects(self):
"""验证 check() 调用 add_collision_box 同步到 MoveIt2。"""
placements = [
{"id": "dev_a", "bbox": (0.6, 0.8), "pos": (1.0, 2.0, 0.5)},
]
self.checker.check(placements)
self.moveit2.add_collision_box.assert_called_once()
call_kwargs = self.moveit2.add_collision_box.call_args
# 验证使用 {device_id}_ 前缀
assert call_kwargs.kwargs["id"] == "dev_a_"
# 验证 size = (w, d, h)
assert call_kwargs.kwargs["size"] == (0.6, 0.8, 0.4)
def test_device_id_prefix(self):
"""碰撞对象名称使用 {device_id}_ 前缀。"""
placements = [
{"id": "robot_arm", "bbox": (0.3, 0.3), "pos": (1.0, 1.0, 0.0)},
{"id": "centrifuge", "bbox": (0.5, 0.5), "pos": (3.0, 3.0, 0.0)},
]
self.checker.check(placements)
calls = self.moveit2.add_collision_box.call_args_list
ids = [c.kwargs["id"] for c in calls]
assert "robot_arm_" in ids
assert "centrifuge_" in ids
def test_sync_failure_does_not_crash(self):
"""add_collision_box 异常不影响碰撞检测结果。"""
self.moveit2.add_collision_box.side_effect = RuntimeError("service unavailable")
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (0.5, 0.5), "pos": (3.0, 3.0, 0.0)},
]
# 不应抛异常
collisions = self.checker.check(placements)
assert collisions == []
def test_check_bounds_within(self):
"""设备在边界内。"""
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
]
assert self.checker.check_bounds(placements, 5.0, 5.0) == []
def test_check_bounds_outside(self):
"""设备超出边界。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (0.2, 0.2, 0.0)},
]
oob = self.checker.check_bounds(placements, 5.0, 5.0)
assert "a" in oob
def test_no_sync_mode(self):
"""sync_to_scene=False 时不调用 add_collision_box。"""
checker = MoveItCollisionChecker(
self.moveit2, sync_to_scene=False,
)
checker._fcl_available = False
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
]
checker.check(placements)
self.moveit2.add_collision_box.assert_not_called()
def test_touching_edges_no_collision(self):
"""恰好边缘接触,不算碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (0.5, 0.5, 0.0)},
{"id": "b", "bbox": (1.0, 1.0), "pos": (1.5, 0.5, 0.0)},
]
collisions = self.checker.check(placements)
assert collisions == []
def test_three_devices_multiple_collisions(self):
"""三个设备,相邻碰撞。"""
placements = [
{"id": "a", "bbox": (1.0, 1.0), "pos": (1.0, 1.0, 0.0)},
{"id": "b", "bbox": (1.0, 1.0), "pos": (1.3, 1.0, 0.0)},
{"id": "c", "bbox": (1.0, 1.0), "pos": (1.6, 1.0, 0.0)},
]
collisions = self.checker.check(placements)
assert ("a", "b") in collisions
assert ("b", "c") in collisions
# ---------- IKFastReachabilityChecker 测试 ----------
class TestIKFastReachabilityCheckerVoxel:
"""体素图模式测试。"""
def _create_voxel_dir(self, tmp_path: Path, arm_id: str = "elite_cs66") -> Path:
"""创建包含体素图的临时目录。"""
# 创建一个简单的体素网格:中心区域可达
grid = np.zeros((100, 100, 50), dtype=bool)
# 标记中心 60x60x30 区域为可达
grid[20:80, 20:80, 10:40] = True
origin = np.array([-0.5, -0.5, 0.0])
resolution = 0.01
npz_path = tmp_path / f"{arm_id}.npz"
np.savez(str(npz_path), grid=grid, origin=origin, resolution=resolution)
return tmp_path
def test_reachable_in_voxel(self, tmp_path):
"""目标在体素图可达区域内。"""
voxel_dir = self._create_voxel_dir(tmp_path)
checker = IKFastReachabilityChecker(voxel_dir=voxel_dir)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
# 中心区域local = (0.0, 0.0, 0.2) → ix=50, iy=50, iz=20 → 可达
target = {"x": 0.0, "y": 0.0, "z": 0.2}
assert checker.is_reachable("elite_cs66", arm_pose, target)
def test_not_reachable_outside_voxel(self, tmp_path):
"""目标在体素图不可达区域。"""
voxel_dir = self._create_voxel_dir(tmp_path)
checker = IKFastReachabilityChecker(voxel_dir=voxel_dir)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
# 边缘区域local = (-0.45, -0.45, 0.0) → ix=5, iy=5, iz=0 → 不可达
target = {"x": -0.45, "y": -0.45, "z": 0.0}
assert not checker.is_reachable("elite_cs66", arm_pose, target)
def test_out_of_bounds_not_reachable(self, tmp_path):
"""目标超出体素图范围。"""
voxel_dir = self._create_voxel_dir(tmp_path)
checker = IKFastReachabilityChecker(voxel_dir=voxel_dir)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 5.0, "y": 5.0, "z": 0.0}
assert not checker.is_reachable("elite_cs66", arm_pose, target)
def test_arm_rotation_transforms_target(self, tmp_path):
"""臂旋转后目标变换到臂坐标系。"""
voxel_dir = self._create_voxel_dir(tmp_path)
checker = IKFastReachabilityChecker(voxel_dir=voxel_dir)
# 臂旋转 90°目标在世界 Y+ 方向 → 臂坐标系 X+ 方向
arm_pose = {"x": 0.0, "y": 0.0, "theta": math.pi / 2}
# 世界 (0, 0.1, 0.2) → 臂坐标系 (0.1, 0, 0.2) → 在可达范围
target = {"x": 0.0, "y": 0.1, "z": 0.2}
assert checker.is_reachable("elite_cs66", arm_pose, target)
def test_unknown_arm_no_voxel_no_moveit(self, tmp_path):
"""未知臂型且无 MoveIt2乐观返回 True。"""
voxel_dir = self._create_voxel_dir(tmp_path)
checker = IKFastReachabilityChecker(voxel_dir=voxel_dir)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 0.5, "y": 0.0, "z": 0.0}
assert checker.is_reachable("unknown_arm", arm_pose, target)
def test_missing_voxel_dir(self):
"""体素目录不存在不报错。"""
checker = IKFastReachabilityChecker(voxel_dir="/nonexistent/path")
assert len(checker._voxel_maps) == 0
class TestIKFastReachabilityCheckerLiveIK:
"""实时 IK 模式测试。"""
def test_reachable_via_ik(self):
"""compute_ik 返回 JointState → 可达。"""
moveit2 = MagicMock()
moveit2.compute_ik.return_value = MagicMock() # 非 None → 成功
checker = IKFastReachabilityChecker(moveit2)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 0.5, "y": 0.0, "z": 0.3}
assert checker.is_reachable("elite_cs66", arm_pose, target)
def test_not_reachable_via_ik(self):
"""compute_ik 返回 None → 不可达。"""
moveit2 = MagicMock()
moveit2.compute_ik.return_value = None
checker = IKFastReachabilityChecker(moveit2)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 5.0, "y": 5.0, "z": 0.0}
assert not checker.is_reachable("elite_cs66", arm_pose, target)
def test_ik_exception_returns_false(self):
"""compute_ik 抛异常 → 不可达。"""
moveit2 = MagicMock()
moveit2.compute_ik.side_effect = RuntimeError("service timeout")
checker = IKFastReachabilityChecker(moveit2)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 0.5, "y": 0.0, "z": 0.0}
assert not checker.is_reachable("elite_cs66", arm_pose, target)
def test_ik_called_with_correct_position(self):
"""验证 compute_ik 接收正确的臂坐标系位置。"""
moveit2 = MagicMock()
moveit2.compute_ik.return_value = MagicMock()
checker = IKFastReachabilityChecker(moveit2)
arm_pose = {"x": 1.0, "y": 2.0, "theta": 0.0}
target = {"x": 1.5, "y": 2.3, "z": 0.4}
checker.is_reachable("elite_cs66", arm_pose, target)
call_kwargs = moveit2.compute_ik.call_args.kwargs
assert call_kwargs["position"] == pytest.approx((0.5, 0.3, 0.4))
def test_voxel_takes_priority_over_live_ik(self, tmp_path):
"""有体素图时优先使用体素查询,不调用 compute_ik。"""
# 创建体素图
grid = np.ones((10, 10, 10), dtype=bool)
origin = np.array([-0.05, -0.05, 0.0])
np.savez(
str(tmp_path / "test_arm.npz"),
grid=grid, origin=origin, resolution=0.01,
)
moveit2 = MagicMock()
checker = IKFastReachabilityChecker(moveit2, voxel_dir=tmp_path)
arm_pose = {"x": 0.0, "y": 0.0, "theta": 0.0}
target = {"x": 0.0, "y": 0.0, "z": 0.05}
checker.is_reachable("test_arm", arm_pose, target)
moveit2.compute_ik.assert_not_called()
# ---------- create_checkers 工厂函数测试 ----------
class TestCreateCheckers:
def test_mock_mode(self):
"""mock 模式返回 Mock 检测器。"""
from ..mock_checkers import (
MockCollisionChecker,
MockReachabilityChecker,
)
collision, reachability = create_checkers(mode="mock")
assert isinstance(collision, MockCollisionChecker)
assert isinstance(reachability, MockReachabilityChecker)
def test_moveit_mode(self):
"""moveit 模式返回 MoveIt2 检测器。"""
moveit2 = MagicMock()
collision, reachability = create_checkers(moveit2, mode="moveit")
assert isinstance(collision, MoveItCollisionChecker)
assert isinstance(reachability, IKFastReachabilityChecker)
def test_moveit_mode_requires_instance(self):
"""moveit 模式无实例时抛异常。"""
with pytest.raises(ValueError, match="MoveIt2 instance required"):
create_checkers(mode="moveit")
def test_default_mode_is_mock(self):
"""默认使用 mock 模式。"""
from ..mock_checkers import MockCollisionChecker
collision, _ = create_checkers()
assert isinstance(collision, MockCollisionChecker)
def test_env_var_override(self, monkeypatch):
"""LAYOUT_CHECKER_MODE 环境变量覆盖默认值。"""
moveit2 = MagicMock()
monkeypatch.setenv("LAYOUT_CHECKER_MODE", "moveit")
collision, _ = create_checkers(moveit2)
assert isinstance(collision, MoveItCollisionChecker)
# ---------- Protocol 兼容性测试 ----------
class TestProtocolConformance:
"""验证适配器满足 Protocol 接口签名。"""
def test_collision_checker_has_check(self):
"""MoveItCollisionChecker 实现 check(placements) 方法。"""
moveit2 = MagicMock()
checker = MoveItCollisionChecker(moveit2, sync_to_scene=False)
checker._fcl_available = False
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
]
result = checker.check(placements)
assert isinstance(result, list)
def test_reachability_checker_has_is_reachable(self):
"""IKFastReachabilityChecker 实现 is_reachable(arm_id, arm_pose, target) 方法。"""
checker = IKFastReachabilityChecker()
result = checker.is_reachable(
"arm_id",
{"x": 0.0, "y": 0.0, "theta": 0.0},
{"x": 0.5, "y": 0.0, "z": 0.0},
)
assert isinstance(result, bool)
def test_collision_checker_has_check_bounds(self):
"""MoveItCollisionChecker 实现 check_bounds 方法。"""
moveit2 = MagicMock()
checker = MoveItCollisionChecker(moveit2, sync_to_scene=False)
placements = [
{"id": "a", "bbox": (0.5, 0.5), "pos": (1.0, 1.0, 0.0)},
]
result = checker.check_bounds(placements, 5.0, 5.0)
assert isinstance(result, list)

View File

@@ -1,113 +0,0 @@
"""Tests for the force-directed seeder engine."""
import math
import pytest
from ..seeders import SeederParams, PRESETS, seed_layout
from ..models import Device, Lab, Placement
class TestSeederParams:
def test_presets_exist(self):
assert "compact_outward" in PRESETS
assert "spread_inward" in PRESETS
assert "row_fallback" in PRESETS
def test_compact_has_negative_boundary(self):
assert PRESETS["compact_outward"].boundary_attraction < 0
def test_spread_has_positive_boundary(self):
assert PRESETS["spread_inward"].boundary_attraction > 0
class TestSeedLayout:
"""seed_layout must return valid placements: within bounds, one per device."""
def _make_devices(self, n: int) -> list[Device]:
return [Device(id=f"d{i}", name=f"Device {i}", bbox=(0.6, 0.4)) for i in range(n)]
def test_returns_one_placement_per_device(self):
devices = self._make_devices(5)
lab = Lab(width=5.0, depth=4.0)
result = seed_layout(devices, lab, PRESETS["compact_outward"])
assert len(result) == 5
ids = {p.device_id for p in result}
assert ids == {f"d{i}" for i in range(5)}
def test_placements_within_bounds(self):
devices = self._make_devices(5)
lab = Lab(width=5.0, depth=4.0)
for preset_name in ["compact_outward", "spread_inward"]:
result = seed_layout(devices, lab, PRESETS[preset_name])
for p in result:
assert 0 <= p.x <= lab.width, f"{preset_name}: x={p.x} out of bounds"
assert 0 <= p.y <= lab.depth, f"{preset_name}: y={p.y} out of bounds"
def test_empty_devices(self):
result = seed_layout([], Lab(width=5, depth=4), PRESETS["compact_outward"])
assert result == []
def test_single_device(self):
devices = self._make_devices(1)
lab = Lab(width=5.0, depth=4.0)
result = seed_layout(devices, lab, PRESETS["compact_outward"])
assert len(result) == 1
assert 0 <= result[0].x <= lab.width
assert 0 <= result[0].y <= lab.depth
def test_row_fallback_delegates(self):
"""row_fallback preset uses generate_fallback, not force engine."""
devices = self._make_devices(3)
lab = Lab(width=5.0, depth=4.0)
# row_fallback is None in PRESETS; seed_layout detects and delegates
result = seed_layout(devices, lab, None) # None = row_fallback
assert len(result) == 3
def test_lab_too_small_returns_results_not_crash(self):
"""When space is insufficient, seeder still returns placements (may have collisions)."""
devices = [Device(id=f"d{i}", name=f"D{i}", bbox=(1.0, 1.0)) for i in range(20)]
lab = Lab(width=2.0, depth=2.0) # Way too small for 20 1m×1m devices
result = seed_layout(devices, lab, PRESETS["compact_outward"])
assert len(result) == 20 # All placed, even if overlapping
for p in result:
assert 0 <= p.x <= lab.width
assert 0 <= p.y <= lab.depth
def test_compact_clusters_toward_center(self):
"""compact_outward should place devices closer to center than spread_inward."""
devices = self._make_devices(4)
lab = Lab(width=8.0, depth=8.0)
center_x, center_y = lab.width / 2, lab.depth / 2
compact = seed_layout(devices, lab, PRESETS["compact_outward"])
spread = seed_layout(devices, lab, PRESETS["spread_inward"])
avg_dist_compact = sum(
math.sqrt((p.x - center_x)**2 + (p.y - center_y)**2) for p in compact
) / len(compact)
avg_dist_spread = sum(
math.sqrt((p.x - center_x)**2 + (p.y - center_y)**2) for p in spread
) / len(spread)
assert avg_dist_compact < avg_dist_spread
class TestOrientation:
"""Orientation modes should set theta based on position relative to center."""
def test_outward_orientation_sets_theta(self):
"""compact_outward: devices should have non-zero theta."""
devices = [
Device(id="a", name="A", bbox=(0.6, 0.4)),
Device(id="b", name="B", bbox=(0.6, 0.4)),
]
lab = Lab(width=5.0, depth=4.0)
result = seed_layout(devices, lab, PRESETS["compact_outward"])
thetas = [p.theta for p in result]
assert any(t != 0.0 for t in thetas) or len(devices) == 1
def test_none_orientation_keeps_zero(self):
"""orientation_mode='none': all thetas stay 0."""
devices = [Device(id="a", name="A", bbox=(0.6, 0.4))]
lab = Lab(width=5.0, depth=4.0)
params = SeederParams(boundary_attraction=0.0, orientation_mode="none")
result = seed_layout(devices, lab, params)
assert result[0].theta == 0.0

View File

@@ -825,6 +825,7 @@ def _extract_class_body(
action_args.setdefault("placeholder_keys", {})
action_args.setdefault("always_free", False)
action_args.setdefault("is_protocol", False)
action_args.setdefault("feedback_interval", 1.0)
action_args.setdefault("description", "")
action_args.setdefault("auto_prefix", False)
action_args.setdefault("parent", False)

View File

@@ -343,6 +343,7 @@ def action(
auto_prefix: bool = False,
parent: bool = False,
node_type: Optional["NodeType"] = None,
feedback_interval: Optional[float] = None,
):
"""
动作方法装饰器
@@ -378,9 +379,16 @@ def action(
"""
def decorator(func: F) -> F:
@wraps(func)
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
import asyncio as _asyncio
if _asyncio.iscoroutinefunction(func):
@wraps(func)
async def wrapper(*args, **kwargs):
return await func(*args, **kwargs)
else:
@wraps(func)
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
# action_type 为哨兵值 => 用户没传, 视为 None (UniLabJsonCommand)
resolved_type = None if action_type is _ACTION_TYPE_UNSET else action_type
@@ -399,6 +407,8 @@ def action(
"auto_prefix": auto_prefix,
"parent": parent,
}
if feedback_interval is not None:
meta["feedback_interval"] = feedback_interval
if node_type is not None:
meta["node_type"] = node_type.value if isinstance(node_type, NodeType) else str(node_type)
wrapper._action_registry_meta = meta # type: ignore[attr-defined]

File diff suppressed because it is too large Load Diff

View File

@@ -31,6 +31,6 @@ hotel.thermo_orbitor_rs2_hotel:
type: object
model:
mesh: thermo_orbitor_rs2_hotel
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/thermo_orbitor_rs2_hotel/macro_device.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/thermo_orbitor_rs2_hotel/macro_device.xacro
type: device
version: 1.0.0

View File

@@ -329,7 +329,7 @@ robotic_arm.SCARA_with_slider.moveit.virtual:
type: object
model:
mesh: arm_slider
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/arm_slider/macro_device.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/arm_slider/macro_device.xacro
type: device
version: 1.0.0
robotic_arm.UR:

View File

@@ -3960,6 +3960,14 @@ virtual_separator:
io_type: source
label: bottom_phase_out
side: SOUTH
- data_key: top_outlet
data_source: executor
data_type: fluid
description: 上相(轻相)液体输出口
handler_key: topphaseout
io_type: source
label: top_phase_out
side: NORTH
- data_key: mechanical_port
data_source: handle
data_type: mechanical

View File

@@ -238,6 +238,7 @@ class Registry:
"class_name": "unilabos_class",
},
"always_free": True,
"feedback_interval": 300.0,
},
"test_latency": test_latency_action,
"auto-test_resource": test_resource_action,
@@ -829,8 +830,9 @@ class Registry:
raw_handles = (action_args or {}).get("handles")
handles = normalize_ast_action_handles(raw_handles) if isinstance(raw_handles, list) else (raw_handles or {})
# placeholder_keys: 优先用装饰器显式配置,否则从参数类型检测
pk = (action_args or {}).get("placeholder_keys") or detect_placeholder_keys(params)
# placeholder_keys: 先从参数类型自动检测,再用装饰器显式配置覆盖/补充
pk = detect_placeholder_keys(params)
pk.update((action_args or {}).get("placeholder_keys") or {})
# 从方法返回值类型生成 result schema
result_schema = None
@@ -852,6 +854,8 @@ class Registry:
}
if (action_args or {}).get("always_free") or method_info.get("always_free"):
entry["always_free"] = True
_fb_iv = (action_args or {}).get("feedback_interval", method_info.get("feedback_interval", 1.0))
entry["feedback_interval"] = _fb_iv
nt = normalize_enum_value((action_args or {}).get("node_type"), NodeType)
if nt:
entry["node_type"] = nt
@@ -975,10 +979,12 @@ class Registry:
"schema": schema,
"goal_default": goal_default,
"handles": handles,
"placeholder_keys": action_args.get("placeholder_keys") or detect_placeholder_keys(method_params),
"placeholder_keys": {**detect_placeholder_keys(method_params), **(action_args.get("placeholder_keys") or {})},
}
if action_args.get("always_free") or method_info.get("always_free"):
action_entry["always_free"] = True
_fb_iv = action_args.get("feedback_interval", method_info.get("feedback_interval", 1.0))
action_entry["feedback_interval"] = _fb_iv
nt = normalize_enum_value(action_args.get("node_type"), NodeType)
if nt:
action_entry["node_type"] = nt

View File

@@ -17,7 +17,7 @@ hplc_plate:
- 0
- 0
- 3.1416
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/hplc_plate/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/hplc_plate/modal.xacro
type: resource
version: 1.0.0
plate_96:
@@ -39,7 +39,7 @@ plate_96:
- 0
- 0
- 0
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/plate_96/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/plate_96/modal.xacro
type: resource
version: 1.0.0
plate_96_high:
@@ -61,7 +61,7 @@ plate_96_high:
- 1.5708
- 0
- 1.5708
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/plate_96_high/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/plate_96_high/modal.xacro
type: resource
version: 1.0.0
tiprack_96_high:
@@ -76,7 +76,7 @@ tiprack_96_high:
init_param_schema: {}
model:
children_mesh: generic_labware_tube_10_75/meshes/0_base.stl
children_mesh_path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/generic_labware_tube_10_75/modal.xacro
children_mesh_path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/generic_labware_tube_10_75/modal.xacro
children_mesh_tf:
- 0.0018
- 0.0018
@@ -92,7 +92,7 @@ tiprack_96_high:
- 1.5708
- 0
- 1.5708
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tiprack_96_high/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tiprack_96_high/modal.xacro
type: resource
version: 1.0.0
tiprack_box:
@@ -107,7 +107,7 @@ tiprack_box:
init_param_schema: {}
model:
children_mesh: tip/meshes/tip.stl
children_mesh_path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tip/modal.xacro
children_mesh_path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tip/modal.xacro
children_mesh_tf:
- 0.0045
- 0.0045
@@ -123,6 +123,6 @@ tiprack_box:
- 0
- 0
- 0
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tiprack_box/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tiprack_box/modal.xacro
type: resource
version: 1.0.0

View File

@@ -11,7 +11,7 @@ bottle_container:
init_param_schema: {}
model:
children_mesh: bottle/meshes/bottle.stl
children_mesh_path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/bottle/modal.xacro
children_mesh_path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/bottle/modal.xacro
children_mesh_tf:
- 0.04
- 0.04
@@ -27,7 +27,7 @@ bottle_container:
- 0
- 0
- 0
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/bottle_container/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/bottle_container/modal.xacro
type: resource
version: 1.0.0
tube_container:
@@ -43,7 +43,7 @@ tube_container:
init_param_schema: {}
model:
children_mesh: tube/meshes/tube.stl
children_mesh_path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tube/modal.xacro
children_mesh_path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tube/modal.xacro
children_mesh_tf:
- 0.017
- 0.017
@@ -59,6 +59,6 @@ tube_container:
- 0
- 0
- 0
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tube_container/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tube_container/modal.xacro
type: resource
version: 1.0.0

View File

@@ -10,6 +10,6 @@ TransformXYZDeck:
init_param_schema: {}
model:
mesh: liquid_transform_xyz
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/liquid_transform_xyz/macro_device.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/liquid_transform_xyz/macro_device.xacro
type: device
version: 1.0.0

View File

@@ -10,7 +10,7 @@ OTDeck:
init_param_schema: {}
model:
mesh: opentrons_liquid_handler
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/opentrons_liquid_handler/macro_device.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/opentrons_liquid_handler/macro_device.xacro
type: device
version: 1.0.0
hplc_station:
@@ -25,6 +25,6 @@ hplc_station:
init_param_schema: {}
model:
mesh: hplc_station
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/hplc_station/macro_device.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/devices/hplc_station/macro_device.xacro
type: device
version: 1.0.0

View File

@@ -109,7 +109,7 @@ nest_96_wellplate_100ul_pcr_full_skirt:
init_param_schema: {}
model:
children_mesh: generic_labware_tube_10_75/meshes/0_base.stl
children_mesh_path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/generic_labware_tube_10_75/modal.xacro
children_mesh_path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/generic_labware_tube_10_75/modal.xacro
children_mesh_tf:
- 0.0018
- 0.0018
@@ -125,7 +125,7 @@ nest_96_wellplate_100ul_pcr_full_skirt:
- -1.5708
- 0
- 1.5708
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tecan_nested_tip_rack/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tecan_nested_tip_rack/modal.xacro
type: resource
version: 1.0.0
nest_96_wellplate_200ul_flat:
@@ -158,7 +158,7 @@ nest_96_wellplate_2ml_deep:
- -1.5708
- 0
- 1.5708
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tecan_nested_tip_rack/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tecan_nested_tip_rack/modal.xacro
type: resource
version: 1.0.0
thermoscientificnunc_96_wellplate_1300ul:

View File

@@ -69,7 +69,7 @@ opentrons_96_filtertiprack_1000ul:
- -1.5708
- 0
- 1.5708
path: https://uni-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tecan_nested_tip_rack/modal.xacro
path: https://leap-lab.oss-cn-zhangjiakou.aliyuncs.com/uni-lab/resources/tecan_nested_tip_rack/modal.xacro
type: resource
version: 1.0.0
opentrons_96_filtertiprack_10ul:

View File

@@ -42,7 +42,7 @@ def canonicalize_nodes_data(
Returns:
ResourceTreeSet: 标准化后的资源树集合
"""
print_status(f"{len(nodes)} Resources loaded", "info")
print_status(f"{len(nodes)} Resources loaded:", "info")
# 第一步基本预处理处理graphml的label字段
outer_host_node_id = None
@@ -1033,7 +1033,7 @@ def resource_plr_to_bioyond(plr_resources: list[ResourcePLR], type_mapping: dict
logger.debug(f"🔍 [PLR→Bioyond] detail转换: {bottle.name} → PLR(x={site['x']},y={site['y']},id={site.get('identifier','?')}) → Bioyond(x={bioyond_x},y={bioyond_y})")
# 🔥 提取物料名称:从 tracker.liquids 中获取第一个液体的名称去除PLR系统添加的后缀
# tracker.liquids 格式: [(物料名称, 数量), ...]
# tracker.liquids 格式: [(物料名称, 数量, 单位), ...]
material_name = bottle_type_info[0] # 默认使用类型名称(如"样品瓶"
if hasattr(bottle, "tracker") and bottle.tracker.liquids:
# 如果有液体,使用液体的名称
@@ -1051,7 +1051,7 @@ def resource_plr_to_bioyond(plr_resources: list[ResourcePLR], type_mapping: dict
"typeId": bottle_type_info[1],
"code": bottle.code if hasattr(bottle, "code") else "",
"name": material_name, # 使用物料名称(如"9090"),而不是类型名称("样品瓶"
"quantity": sum(qty for _, qty in bottle.tracker.liquids) if hasattr(bottle, "tracker") else 0,
"quantity": sum(qty for _, qty, *_ in bottle.tracker.liquids) if hasattr(bottle, "tracker") else 0,
"x": bioyond_x,
"y": bioyond_y,
"z": 1,
@@ -1124,7 +1124,7 @@ def resource_plr_to_bioyond(plr_resources: list[ResourcePLR], type_mapping: dict
"barCode": "",
"name": material_name, # 使用物料名称而不是资源名称
"unit": default_unit, # 使用配置的单位或默认单位
"quantity": sum(qty for _, qty in bottle.tracker.liquids) if hasattr(bottle, "tracker") else 0,
"quantity": sum(qty for _, qty, *_ in bottle.tracker.liquids) if hasattr(bottle, "tracker") else 0,
"Parameters": parameters_json # API 实际要求的字段(必需)
}

View File

@@ -4,6 +4,8 @@ import json
import threading
import time
import traceback
from unilabos.utils.tools import fast_dumps_str as _fast_dumps_str, fast_loads as _fast_loads
from typing import (
get_type_hints,
TypeVar,
@@ -78,6 +80,67 @@ if TYPE_CHECKING:
T = TypeVar("T")
class RclpyAsyncMutex:
"""rclpy executor 兼容的异步互斥锁
通过 executor.create_task 唤醒等待者,避免 timer 的 InvalidHandle 问题。
"""
def __init__(self, name: str = ""):
self._lock = threading.Lock()
self._acquired = False
self._queue: List[Future] = []
self._name = name
self._holder: Optional[str] = None
async def acquire(self, node: "BaseROS2DeviceNode", tag: str = ""):
"""获取锁。如果已被占用,则异步等待直到锁释放。"""
# t0 = time.time()
with self._lock:
# qlen = len(self._queue)
if not self._acquired:
self._acquired = True
self._holder = tag
# node.lab_logger().debug(
# f"[Mutex:{self._name}] 获取锁 tag={tag} (无等待, queue=0)"
# )
return
waiter = Future()
self._queue.append(waiter)
# node.lab_logger().info(
# f"[Mutex:{self._name}] 等待锁 tag={tag} "
# f"(holder={self._holder}, queue={qlen + 1})"
# )
await waiter
# wait_ms = (time.time() - t0) * 1000
self._holder = tag
# node.lab_logger().info(
# f"[Mutex:{self._name}] 获取锁 tag={tag} (等了 {wait_ms:.0f}ms)"
# )
def release(self, node: "BaseROS2DeviceNode"):
"""释放锁,通过 executor task 唤醒下一个等待者。"""
with self._lock:
# old_holder = self._holder
if self._queue:
next_waiter = self._queue.pop(0)
# node.lab_logger().debug(
# f"[Mutex:{self._name}] 释放锁 holder={old_holder} → 唤醒下一个 (剩余 queue={len(self._queue)})"
# )
async def _wake():
if not next_waiter.done():
next_waiter.set_result(None)
rclpy.get_global_executor().create_task(_wake())
else:
self._acquired = False
self._holder = None
# node.lab_logger().debug(
# f"[Mutex:{self._name}] 释放锁 holder={old_holder} → 空闲"
# )
# 在线设备注册表
registered_devices: Dict[str, "DeviceInfoType"] = {}
@@ -355,6 +418,8 @@ class BaseROS2DeviceNode(Node, Generic[T]):
max_workers=max(len(action_value_mappings), 1), thread_name_prefix=f"ROSDevice{self.device_id}"
)
self._append_resource_lock = RclpyAsyncMutex(name=f"AR:{device_id}")
# 创建资源管理客户端
self._resource_clients: Dict[str, Client] = {
"resource_add": self.create_client(ResourceAdd, "/resources/add", callback_group=self.callback_group),
@@ -378,15 +443,40 @@ class BaseROS2DeviceNode(Node, Generic[T]):
return res
async def append_resource(req: SerialCommand_Request, res: SerialCommand_Response):
_cmd = _fast_loads(req.command)
_res_name = _cmd.get("resource", [{}])
_res_name = (_res_name[0].get("id", "?") if isinstance(_res_name, list) and _res_name
else _res_name.get("id", "?") if isinstance(_res_name, dict) else "?")
_ar_tag = f"{_res_name}"
# _t_enter = time.time()
# self.lab_logger().info(f"[AR:{_ar_tag}] 进入 append_resource")
await self._append_resource_lock.acquire(self, tag=_ar_tag)
# _t_locked = time.time()
try:
return await _append_resource_inner(req, res, _ar_tag)
# _t_done = time.time()
# self.lab_logger().info(
# f"[AR:{_ar_tag}] 完成 "
# f"等锁={(_t_locked - _t_enter) * 1000:.0f}ms "
# f"执行={(_t_done - _t_locked) * 1000:.0f}ms "
# f"总计={(_t_done - _t_enter) * 1000:.0f}ms"
# )
except Exception as _ex:
self.lab_logger().error(f"[AR:{_ar_tag}] 异常: {_ex}")
raise
finally:
self._append_resource_lock.release(self)
async def _append_resource_inner(req: SerialCommand_Request, res: SerialCommand_Response, _ar_tag: str = ""):
from pylabrobot.resources.deck import Deck
from pylabrobot.resources import Coordinate
from pylabrobot.resources import Plate
# 物料传输到对应的node节点
# _t0 = time.time()
client = self._resource_clients["c2s_update_resource_tree"]
request = SerialCommand.Request()
request2 = SerialCommand.Request()
command_json = json.loads(req.command)
command_json = _fast_loads(req.command)
namespace = command_json["namespace"]
bind_parent_id = command_json["bind_parent_id"]
edge_device_id = command_json["edge_device_id"]
@@ -439,7 +529,11 @@ class BaseROS2DeviceNode(Node, Generic[T]):
f"更新物料{container_instance.name}出现不支持的数据类型{type(found_resource)} {found_resource}"
)
# noinspection PyUnresolvedReferences
request.command = json.dumps(
# _t1 = time.time()
# self.lab_logger().debug(
# f"[AR:{_ar_tag}] 准备完成 PLR转换+序列化 {((_t1 - _t0) * 1000):.0f}ms, 发送首次上传..."
# )
request.command = _fast_dumps_str(
{
"action": "add",
"data": {
@@ -450,7 +544,11 @@ class BaseROS2DeviceNode(Node, Generic[T]):
}
)
tree_response: SerialCommand.Response = await client.call_async(request)
uuid_maps = json.loads(tree_response.response)
# _t2 = time.time()
# self.lab_logger().debug(
# f"[AR:{_ar_tag}] 首次上传完成 {((_t2 - _t1) * 1000):.0f}ms"
# )
uuid_maps = _fast_loads(tree_response.response)
plr_instances = rts.to_plr_resources()
for plr_instance in plr_instances:
self.resource_tracker.loop_update_uuid(plr_instance, uuid_maps)
@@ -508,7 +606,7 @@ class BaseROS2DeviceNode(Node, Generic[T]):
for input_well, liquid_type, liquid_volume, liquid_input_slot in zip(
input_wells, ADD_LIQUID_TYPE, LIQUID_VOLUME, LIQUID_INPUT_SLOT
):
input_well.set_liquids([(liquid_type, liquid_volume, "uL")])
input_well.set_liquids([(liquid_type, liquid_volume, "ul")])
final_response["liquid_input_resource_tree"] = ResourceTreeSet.from_plr_resources(
input_wells
).dump()
@@ -527,12 +625,13 @@ class BaseROS2DeviceNode(Node, Generic[T]):
Coordinate(location["x"], location["y"], location["z"]),
**other_calling_param,
)
# 调整了液体以及Deck之后要重新Assign
# noinspection PyUnresolvedReferences
# _t3 = time.time()
rts_with_parent = ResourceTreeSet.from_plr_resources([parent_resource])
# _n_parent = len(rts_with_parent.all_nodes)
if rts_with_parent.root_nodes[0].res_content.uuid_parent is None:
rts_with_parent.root_nodes[0].res_content.parent_uuid = self.uuid
request.command = json.dumps(
request.command = _fast_dumps_str(
{
"action": "add",
"data": {
@@ -542,11 +641,18 @@ class BaseROS2DeviceNode(Node, Generic[T]):
},
}
)
# _t4 = time.time()
# self.lab_logger().debug(
# f"[AR:{_ar_tag}] 二次上传序列化 {_n_parent}节点 {((_t4 - _t3) * 1000):.0f}ms, 发送中..."
# )
tree_response: SerialCommand.Response = await client.call_async(request)
uuid_maps = json.loads(tree_response.response)
# _t5 = time.time()
uuid_maps = _fast_loads(tree_response.response)
self.resource_tracker.loop_update_uuid(input_resources, uuid_maps)
self._lab_logger.info(f"Resource tree added. UUID mapping: {len(uuid_maps)} nodes")
# 这里created_resources不包含parent_resource
# self._lab_logger.info(
# f"[AR:{_ar_tag}] 二次上传完成 HTTP={(_t5 - _t4) * 1000:.0f}ms "
# f"UUID映射={len(uuid_maps)}节点 总执行={(_t5 - _t0) * 1000:.0f}ms"
# )
# 发送给ResourceMeshManager
action_client = ActionClient(
self,
@@ -683,7 +789,11 @@ class BaseROS2DeviceNode(Node, Generic[T]):
)
# 发送请求并等待响应
response: SerialCommand_Response = await self._resource_clients["resource_get"].call_async(r)
if not response.response:
raise ValueError(f"查询资源 {resource_id} 失败:服务端返回空响应")
raw_data = json.loads(response.response)
if not raw_data:
raise ValueError(f"查询资源 {resource_id} 失败:返回数据为空")
# 转换为 PLR 资源
tree_set = ResourceTreeSet.from_raw_dict_list(raw_data)
@@ -1132,7 +1242,8 @@ class BaseROS2DeviceNode(Node, Generic[T]):
if uid is None:
raise ValueError(f"目标物料{target_resource}没有unilabos_uuid属性无法转运")
target_uids.append(uid)
srv_address = f"/srv{target_device_id}/s2c_resource_tree"
_ns = target_device_id if target_device_id.startswith("/devices/") else f"/devices/{target_device_id.lstrip('/')}"
srv_address = f"/srv{_ns}/s2c_resource_tree"
sclient = self.create_client(SerialCommand, srv_address)
# 等待服务可用(设置超时)
if not sclient.wait_for_service(timeout_sec=5.0):
@@ -1182,7 +1293,7 @@ class BaseROS2DeviceNode(Node, Generic[T]):
return False
time.sleep(0.05)
self.lab_logger().info(f"资源本地增加到{target_device_id}结果: {response.response}")
return None
return "转运完成"
def register_device(self):
"""向注册表中注册设备信息"""
@@ -1567,37 +1678,75 @@ class BaseROS2DeviceNode(Node, Generic[T]):
feedback_msg_types = action_type.Feedback.get_fields_and_field_types()
result_msg_types = action_type.Result.get_fields_and_field_types()
while future is not None and not future.done():
if goal_handle.is_cancel_requested:
self.lab_logger().info(f"取消动作: {action_name}")
future.cancel() # 尝试取消线程池中的任务
goal_handle.canceled()
return action_type.Result()
# 低频 feedback timer10s不阻塞完成检测
_feedback_timer = None
self._time_spent = time.time() - time_start
self._time_remaining = time_overall - self._time_spent
def _publish_feedback():
if future is not None and not future.done():
self._time_spent = time.time() - time_start
self._time_remaining = time_overall - self._time_spent
feedback_values = {}
for msg_name, attr_name in action_value_mapping["feedback"].items():
if hasattr(self.driver_instance, f"get_{attr_name}"):
method = getattr(self.driver_instance, f"get_{attr_name}")
if not asyncio.iscoroutinefunction(method):
feedback_values[msg_name] = method()
elif hasattr(self.driver_instance, attr_name):
feedback_values[msg_name] = getattr(self.driver_instance, attr_name)
if self._print_publish:
self.lab_logger().info(f"反馈: {feedback_values}")
feedback_msg = convert_to_ros_msg_with_mapping(
ros_msg_type=action_type.Feedback(),
obj=feedback_values,
value_mapping=action_value_mapping["feedback"],
)
goal_handle.publish_feedback(feedback_msg)
# 发布反馈
feedback_values = {}
for msg_name, attr_name in action_value_mapping["feedback"].items():
if hasattr(self.driver_instance, f"get_{attr_name}"):
method = getattr(self.driver_instance, f"get_{attr_name}")
if not asyncio.iscoroutinefunction(method):
feedback_values[msg_name] = method()
elif hasattr(self.driver_instance, attr_name):
feedback_values[msg_name] = getattr(self.driver_instance, attr_name)
if self._print_publish:
self.lab_logger().info(f"反馈: {feedback_values}")
feedback_msg = convert_to_ros_msg_with_mapping(
ros_msg_type=action_type.Feedback(),
obj=feedback_values,
value_mapping=action_value_mapping["feedback"],
if action_value_mapping.get("feedback"):
_fb_interval = action_value_mapping.get("feedback_interval", 0.5)
_feedback_timer = self.create_timer(
_fb_interval, _publish_feedback, callback_group=self.callback_group
)
goal_handle.publish_feedback(feedback_msg)
time.sleep(0.5)
# 等待 action 完成
if future is not None:
if isinstance(future, Task):
# rclpy Task直接 await完成瞬间唤醒
try:
_raw_result = await future
except Exception as e:
_raw_result = e
else:
# concurrent.futures.Future同步 action用 rclpy 兼容的轮询
_poll_future = Future()
def _on_sync_done(fut):
if not _poll_future.done():
_poll_future.set_result(None)
future.add_done_callback(_on_sync_done)
await _poll_future
try:
_raw_result = future.result()
except Exception as e:
_raw_result = e
# 确保 execution_error/success 被正确设置(不依赖 done callback 时序)
if isinstance(_raw_result, BaseException):
if not execution_error:
execution_error = traceback.format_exception(
type(_raw_result), _raw_result, _raw_result.__traceback__
)
execution_error = "".join(execution_error)
execution_success = False
action_return_value = _raw_result
elif not execution_error:
execution_success = True
action_return_value = _raw_result
# 清理 feedback timer
if _feedback_timer is not None:
_feedback_timer.cancel()
if future is not None and future.cancelled():
self.lab_logger().info(f"动作 {action_name} 已取消")
@@ -1606,8 +1755,12 @@ class BaseROS2DeviceNode(Node, Generic[T]):
# self.lab_logger().info(f"动作执行完成: {action_name}")
del future
# 执行失败时跳过物料状态更新
if execution_error:
execution_success = False
# 向Host更新物料当前状态
if action_name not in ["create_resource_detailed", "create_resource"]:
if not execution_error and action_name not in ["create_resource_detailed", "create_resource"]:
for k, v in goal.get_fields_and_field_types().items():
if v not in ["unilabos_msgs/Resource", "sequence<unilabos_msgs/Resource>"]:
continue
@@ -1663,7 +1816,7 @@ class BaseROS2DeviceNode(Node, Generic[T]):
for attr_name in result_msg_types.keys():
if attr_name in ["success", "reached_goal"]:
setattr(result_msg, attr_name, True)
setattr(result_msg, attr_name, execution_success)
elif attr_name == "return_info":
setattr(
result_msg,
@@ -1769,7 +1922,7 @@ class BaseROS2DeviceNode(Node, Generic[T]):
raise ValueError("至少需要提供一个 UUID")
uuids_list = list(uuids)
future = self._resource_clients["c2s_update_resource_tree"].call_async(
future: Future = self._resource_clients["c2s_update_resource_tree"].call_async(
SerialCommand.Request(
command=json.dumps(
{
@@ -1795,6 +1948,8 @@ class BaseROS2DeviceNode(Node, Generic[T]):
raise Exception(f"资源查询返回空结果: {uuids_list}")
raw_data = json.loads(response.response)
if not raw_data:
raise Exception(f"资源原始查询返回空结果: {raw_data}")
# 转换为 PLR 资源
tree_set = ResourceTreeSet.from_raw_dict_list(raw_data)
@@ -1816,10 +1971,15 @@ class BaseROS2DeviceNode(Node, Generic[T]):
mapped_plr_resources = []
for uuid in uuids_list:
found = None
for plr_resource in figured_resources:
r = self.resource_tracker.loop_find_with_uuid(plr_resource, uuid)
mapped_plr_resources.append(r)
break
if r is not None:
found = r
break
if found is None:
raise Exception(f"未能在已解析的资源树中找到 uuid={uuid} 对应的资源")
mapped_plr_resources.append(found)
return mapped_plr_resources
@@ -1912,16 +2072,27 @@ class BaseROS2DeviceNode(Node, Generic[T]):
f"执行动作时JSON缺少function_name或function_args: {ex}\n原JSON: {string}\n{traceback.format_exc()}"
)
async def _convert_resource_async(self, resource_data: Dict[str, Any]):
"""异步转换资源数据为实例"""
# 使用封装的get_resource_with_dir方法获取PLR资源
plr_resource = await self.get_resource_with_dir(resource_ids=resource_data["id"], with_children=True)
async def _convert_resource_async(self, resource_data: "ResourceDictType"):
"""异步转换 ResourceDictType 为 PLR 实例,优先用 uuid 查询"""
unilabos_uuid = resource_data.get("uuid")
if unilabos_uuid:
resource_tree = await self.get_resource([unilabos_uuid], with_children=True)
plr_resources = resource_tree.to_plr_resources()
if plr_resources:
plr_resource = plr_resources[0]
else:
raise ValueError(f"通过 uuid={unilabos_uuid} 查询资源为空")
else:
res_id = resource_data.get("id") or resource_data.get("name", "")
if not res_id:
raise ValueError(f"资源数据缺少 uuid 和 id: {list(resource_data.keys())}")
plr_resource = await self.get_resource_with_dir(resource_id=res_id, with_children=True)
# 通过资源跟踪器获取本地实例
res = self.resource_tracker.figure_resource(plr_resource, try_mode=True)
if len(res) == 0:
# todo: 后续通过decoration来区分减少warning
self.lab_logger().warning(f"资源转换未能索引到实例: {resource_data},返回新建实例")
self.lab_logger().warning(f"资源转换未能索引到实例: {resource_data.get('id', '?')},返回新建实例")
return plr_resource
elif len(res) == 1:
return res[0]

View File

@@ -4,6 +4,8 @@ import threading
import time
import traceback
import uuid
from unilabos.utils.tools import fast_dumps_str as _fast_dumps_str, fast_loads as _fast_loads
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Optional, Dict, Any, List, ClassVar, Set, Union
@@ -618,22 +620,17 @@ class HostNode(BaseROS2DeviceNode):
}
)
]
response: List[str] = await self.create_resource_detailed(
resources, device_ids, bind_parent_id, bind_location, other_calling_param
)
try:
assert len(response) == 1, "Create Resource应当只返回一个结果"
for i in response:
res = json.loads(i)
if "suc" in res:
raise ValueError(res.get("error"))
return res
except Exception as ex:
pass
_n = "\n"
raise ValueError(f"创建资源时失败!\n{_n.join(response)}")
assert len(response) == 1, "Create Resource应当只返回一个结果"
for i in response:
res = json.loads(i)
if "suc" in res and not res["suc"]:
raise ValueError(res.get("error", "未知错误"))
return res
raise ValueError(f"创建资源时失败!响应为空")
def initialize_device(self, device_id: str, device_config: ResourceDictInstance) -> None:
"""
@@ -1168,7 +1165,7 @@ class HostNode(BaseROS2DeviceNode):
else:
physical_setup_graph.nodes[resource_dict["id"]]["data"].update(resource_dict.get("data", {}))
response.response = json.dumps(uuid_mapping) if success else "FAILED"
response.response = _fast_dumps_str(uuid_mapping) if success else "FAILED"
self.lab_logger().info(f"[Host Node-Resource] Resource tree add completed, success: {success}")
async def _resource_tree_action_get_callback(self, data: dict, response: SerialCommand_Response): # OK
@@ -1178,6 +1175,7 @@ class HostNode(BaseROS2DeviceNode):
resource_response = http_client.resource_tree_get(uuid_list, with_children)
response.response = json.dumps(resource_response)
self.lab_logger().trace(f"[Host Node-Resource] Resource tree get request callback {response.response}")
async def _resource_tree_action_remove_callback(self, data: dict, response: SerialCommand_Response):
"""
@@ -1230,9 +1228,26 @@ class HostNode(BaseROS2DeviceNode):
"""
try:
# 解析请求数据
data = json.loads(request.command)
data = _fast_loads(request.command)
action = data["action"]
self.lab_logger().info(f"[Host Node-Resource] Resource tree {action} request received")
inner = data.get("data", {})
if action == "add":
mount_uuid = inner.get("mount_uuid", "?")[:8] if isinstance(inner, dict) else "?"
tree_data = inner.get("data", []) if isinstance(inner, dict) else inner
node_count = len(tree_data) if isinstance(tree_data, list) else "?"
source = f"mount={mount_uuid}.. nodes≈{node_count}"
elif action in ("get", "remove"):
uid_list = inner.get("data", inner) if isinstance(inner, dict) else inner
source = f"uuids={len(uid_list) if isinstance(uid_list, list) else '?'}"
elif action == "update":
tree_data = inner.get("data", []) if isinstance(inner, dict) else inner
node_count = len(tree_data) if isinstance(tree_data, list) else "?"
source = f"nodes≈{node_count}"
else:
source = ""
self.lab_logger().info(
f"[Host Node-Resource] Resource tree {action} request received ({source})"
)
data = data["data"]
if action == "add":
await self._resource_tree_action_add_callback(data, response)

View File

@@ -22,6 +22,447 @@
"arm_state": "idle",
"message": "工作台就绪"
}
},
{
"id": "PRCXI",
"name": "PRCXI",
"type": "device",
"class": "liquid_handler.prcxi",
"parent": "",
"pose": {
"size": {
"width": 562,
"height": 394,
"depth": 0
}
},
"config": {
"axis": "Left",
"deck": {
"_resource_type": "unilabos.devices.liquid_handling.prcxi.prcxi:PRCXI9300Deck",
"_resource_child_name": "PRCXI_Deck"
},
"host": "10.20.30.184",
"port": 9999,
"debug": true,
"setup": true,
"is_9320": true,
"timeout": 10,
"matrix_id": "5de524d0-3f95-406c-86dd-f83626ebc7cb",
"simulator": true,
"channel_num": 2
},
"data": {
"reset_ok": true
},
"schema": {},
"description": "",
"model": null,
"position": {
"x": 0,
"y": 240,
"z": 0
}
},
{
"id": "PRCXI_Deck",
"name": "PRCXI_Deck",
"children": [],
"parent": "PRCXI",
"type": "deck",
"class": "",
"position": {
"x": 10,
"y": 10,
"z": 0
},
"config": {
"type": "PRCXI9300Deck",
"size_x": 542,
"size_y": 374,
"size_z": 0,
"rotation": {
"x": 0,
"y": 0,
"z": 0,
"type": "Rotation"
},
"category": "deck",
"barcode": null,
"preferred_pickup_location": null,
"sites": [
{
"label": "T1",
"visible": true,
"occupied_by": null,
"position": {
"x": 0,
"y": 0,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"container",
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T2",
"visible": true,
"occupied_by": null,
"position": {
"x": 138,
"y": 0,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T3",
"visible": true,
"occupied_by": null,
"position": {
"x": 276,
"y": 0,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T4",
"visible": true,
"occupied_by": null,
"position": {
"x": 414,
"y": 0,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T5",
"visible": true,
"occupied_by": null,
"position": {
"x": 0,
"y": 96,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T6",
"visible": true,
"occupied_by": null,
"position": {
"x": 138,
"y": 96,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T7",
"visible": true,
"occupied_by": null,
"position": {
"x": 276,
"y": 96,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T8",
"visible": true,
"occupied_by": null,
"position": {
"x": 414,
"y": 96,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T9",
"visible": true,
"occupied_by": null,
"position": {
"x": 0,
"y": 192,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T10",
"visible": true,
"occupied_by": null,
"position": {
"x": 138,
"y": 192,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T11",
"visible": true,
"occupied_by": null,
"position": {
"x": 276,
"y": 192,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T12",
"visible": true,
"occupied_by": null,
"position": {
"x": 414,
"y": 192,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T13",
"visible": true,
"occupied_by": null,
"position": {
"x": 0,
"y": 288,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T14",
"visible": true,
"occupied_by": null,
"position": {
"x": 138,
"y": 288,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T15",
"visible": true,
"occupied_by": null,
"position": {
"x": 276,
"y": 288,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
},
{
"label": "T16",
"visible": true,
"occupied_by": null,
"position": {
"x": 414,
"y": 288,
"z": 0
},
"size": {
"width": 128.0,
"height": 86,
"depth": 0
},
"content_type": [
"plate",
"tip_rack",
"plates",
"tip_racks",
"tube_rack",
"adaptor"
]
}
]
},
"data": {}
}
],
"links": []

View File

@@ -188,7 +188,13 @@ class EnvironmentChecker:
"crcmod": "crcmod-plus",
}
self.special_packages = {"pylabrobot": "git+https://github.com/Xuwznln/pylabrobot.git"}
# 中文 locale 下走 Gitee 镜像,规避 GitHub 拉取失败
pylabrobot_url = (
"git+https://gitee.com/xuwznln/pylabrobot.git"
if _is_chinese_locale()
else "git+https://github.com/Xuwznln/pylabrobot.git"
)
self.special_packages = {"pylabrobot": pylabrobot_url}
self.version_requirements = {
"msgcenterpy": "0.1.8",

View File

@@ -17,6 +17,14 @@ try:
default=json_default,
)
def fast_loads(data) -> dict:
"""JSON 反序列化,优先使用 orjson。接受 str / bytes。"""
return orjson.loads(data)
def fast_dumps_str(obj, **kwargs) -> str:
"""JSON 序列化为 str优先使用 orjson。用于需要 str 而非 bytes 的场景(如 ROS msg"""
return orjson.dumps(obj, option=orjson.OPT_NON_STR_KEYS, default=json_default).decode("utf-8")
def normalize_json(info: dict) -> dict:
"""经 JSON 序列化/反序列化一轮来清理非标准类型。"""
return orjson.loads(orjson.dumps(info, default=json_default))
@@ -29,6 +37,14 @@ except ImportError:
def fast_dumps_pretty(obj, **kwargs) -> bytes: # type: ignore[misc]
return json.dumps(obj, indent=2, ensure_ascii=False, cls=TypeEncoder).encode("utf-8")
def fast_loads(data) -> dict: # type: ignore[misc]
if isinstance(data, bytes):
data = data.decode("utf-8")
return json.loads(data)
def fast_dumps_str(obj, **kwargs) -> str: # type: ignore[misc]
return json.dumps(obj, ensure_ascii=False, cls=TypeEncoder)
def normalize_json(info: dict) -> dict: # type: ignore[misc]
return json.loads(json.dumps(info, ensure_ascii=False, cls=TypeEncoder))

View File

@@ -1,241 +0,0 @@
import ast
import json
from typing import Dict, List, Any, Tuple, Optional
from .common import WorkflowGraph, RegistryAdapter
Json = Dict[str, Any]
# ---------------- Converter ----------------
class DeviceMethodConverter:
"""
- 字段统一resource_name原 device_class、template_name原 action_key
- params 单层inputs 使用 'params.' 前缀
- SimpleGraph.add_workflow_node 负责变量连线与边
"""
def __init__(self, device_registry: Optional[Dict[str, Any]] = None):
self.graph = WorkflowGraph()
self.variable_sources: Dict[str, Dict[str, Any]] = {} # var -> {node_id, output_name}
self.instance_to_resource: Dict[str, Optional[str]] = {} # 实例名 -> resource_name
self.node_id_counter: int = 0
self.registry = RegistryAdapter(device_registry or {})
# ---- helpers ----
def _new_node_id(self) -> int:
nid = self.node_id_counter
self.node_id_counter += 1
return nid
def _assign_targets(self, targets) -> List[str]:
names: List[str] = []
import ast
if isinstance(targets, ast.Tuple):
for elt in targets.elts:
if isinstance(elt, ast.Name):
names.append(elt.id)
elif isinstance(targets, ast.Name):
names.append(targets.id)
return names
def _extract_device_instantiation(self, node) -> Optional[Tuple[str, str]]:
import ast
if not isinstance(node.value, ast.Call):
return None
callee = node.value.func
if isinstance(callee, ast.Name):
class_name = callee.id
elif isinstance(callee, ast.Attribute) and isinstance(callee.value, ast.Name):
class_name = callee.attr
else:
return None
if isinstance(node.targets[0], ast.Name):
instance = node.targets[0].id
return instance, class_name
return None
def _extract_call(self, call) -> Tuple[str, str, Dict[str, Any], str]:
import ast
owner_name, method_name, call_kind = "", "", "func"
if isinstance(call.func, ast.Attribute):
method_name = call.func.attr
if isinstance(call.func.value, ast.Name):
owner_name = call.func.value.id
call_kind = "instance" if owner_name in self.instance_to_resource else "class_or_module"
elif isinstance(call.func.value, ast.Attribute) and isinstance(call.func.value.value, ast.Name):
owner_name = call.func.value.attr
call_kind = "class_or_module"
elif isinstance(call.func, ast.Name):
method_name = call.func.id
call_kind = "func"
def pack(node):
if isinstance(node, ast.Name):
return {"type": "variable", "value": node.id}
if isinstance(node, ast.Constant):
return {"type": "constant", "value": node.value}
if isinstance(node, ast.Dict):
return {"type": "dict", "value": self._parse_dict(node)}
if isinstance(node, ast.List):
return {"type": "list", "value": self._parse_list(node)}
return {"type": "raw", "value": ast.unparse(node) if hasattr(ast, "unparse") else str(node)}
args: Dict[str, Any] = {}
pos: List[Any] = []
for a in call.args:
pos.append(pack(a))
for kw in call.keywords:
args[kw.arg] = pack(kw.value)
if pos:
args["_positional"] = pos
return owner_name, method_name, args, call_kind
def _parse_dict(self, node) -> Dict[str, Any]:
import ast
out: Dict[str, Any] = {}
for k, v in zip(node.keys, node.values):
if isinstance(k, ast.Constant):
key = str(k.value)
if isinstance(v, ast.Name):
out[key] = f"var:{v.id}"
elif isinstance(v, ast.Constant):
out[key] = v.value
elif isinstance(v, ast.Dict):
out[key] = self._parse_dict(v)
elif isinstance(v, ast.List):
out[key] = self._parse_list(v)
return out
def _parse_list(self, node) -> List[Any]:
import ast
out: List[Any] = []
for elt in node.elts:
if isinstance(elt, ast.Name):
out.append(f"var:{elt.id}")
elif isinstance(elt, ast.Constant):
out.append(elt.value)
elif isinstance(elt, ast.Dict):
out.append(self._parse_dict(elt))
elif isinstance(elt, ast.List):
out.append(self._parse_list(elt))
return out
def _normalize_var_tokens(self, x: Any) -> Any:
if isinstance(x, str) and x.startswith("var:"):
return {"__var__": x[4:]}
if isinstance(x, list):
return [self._normalize_var_tokens(i) for i in x]
if isinstance(x, dict):
return {k: self._normalize_var_tokens(v) for k, v in x.items()}
return x
def _make_params_payload(self, resource_name: Optional[str], template_name: str, call_args: Dict[str, Any]) -> Dict[str, Any]:
input_keys = self.registry.get_action_input_keys(resource_name, template_name) if resource_name else []
defaults = self.registry.get_action_goal_default(resource_name, template_name) if resource_name else {}
params: Dict[str, Any] = dict(defaults)
def unpack(p):
t, v = p.get("type"), p.get("value")
if t == "variable":
return {"__var__": v}
if t == "dict":
return self._normalize_var_tokens(v)
if t == "list":
return self._normalize_var_tokens(v)
return v
for k, p in call_args.items():
if k == "_positional":
continue
params[k] = unpack(p)
pos = call_args.get("_positional", [])
if pos:
if input_keys:
for i, p in enumerate(pos):
if i >= len(input_keys):
break
name = input_keys[i]
if name in params:
continue
params[name] = unpack(p)
else:
for i, p in enumerate(pos):
params[f"arg_{i}"] = unpack(p)
return params
# ---- handlers ----
def _on_assign(self, stmt):
import ast
inst = self._extract_device_instantiation(stmt)
if inst:
instance, code_class = inst
resource_name = self.registry.resolve_resource_by_classname(code_class)
self.instance_to_resource[instance] = resource_name
return
if isinstance(stmt.value, ast.Call):
owner, method, call_args, kind = self._extract_call(stmt.value)
if kind == "instance":
device_key = owner
resource_name = self.instance_to_resource.get(owner)
else:
device_key = owner
resource_name = self.registry.resolve_resource_by_classname(owner)
module = self.registry.get_device_module(resource_name)
params = self._make_params_payload(resource_name, method, call_args)
nid = self._new_node_id()
self.graph.add_workflow_node(
nid,
device_key=device_key,
resource_name=resource_name, # ✅
module=module,
template_name=method, # ✅
params=params,
variable_sources=self.variable_sources,
add_ready_if_no_vars=True,
prev_node_id=(nid - 1) if nid > 0 else None,
)
out_vars = self._assign_targets(stmt.targets[0])
for var in out_vars:
self.variable_sources[var] = {"node_id": nid, "output_name": "result"}
def _on_expr(self, stmt):
import ast
if not isinstance(stmt.value, ast.Call):
return
owner, method, call_args, kind = self._extract_call(stmt.value)
if kind == "instance":
device_key = owner
resource_name = self.instance_to_resource.get(owner)
else:
device_key = owner
resource_name = self.registry.resolve_resource_by_classname(owner)
module = self.registry.get_device_module(resource_name)
params = self._make_params_payload(resource_name, method, call_args)
nid = self._new_node_id()
self.graph.add_workflow_node(
nid,
device_key=device_key,
resource_name=resource_name, # ✅
module=module,
template_name=method, # ✅
params=params,
variable_sources=self.variable_sources,
add_ready_if_no_vars=True,
prev_node_id=(nid - 1) if nid > 0 else None,
)
def convert(self, python_code: str):
tree = ast.parse(python_code)
for stmt in tree.body:
if isinstance(stmt, ast.Assign):
self._on_assign(stmt)
elif isinstance(stmt, ast.Expr):
self._on_expr(stmt)
return self

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