mirror of
https://github.com/deepmodeling/Uni-Lab-OS
synced 2026-05-23 21:26:04 +00:00
9ef24b7768
Replace scipy differential_evolution with custom DE loop for per-device crossover, circular θ wrapping, and configurable mutation strategy (currenttobest1bin default, best1bin as turbo mode). Key improvements: - Graduate ALL hard constraints during DE (proportional penalty instead of flat inf), giving DE smooth gradient for reachability, min_spacing, etc. Binary inf preserved for final pass/fail reporting. - 2-axis sweep-and-prune AABB broad phase for collision pair pruning - Multi-seed injection from multiple seeder presets + Gaussian variants - snap_theta_safe: collision-check after angle snapping, revert on violation - Weight normalization (100 distance / 60 angle / 5× hard multiplier) - Constraint priority field (critical/high/normal/low → weight multiplier) with LLM intent interpreter setting priority per constraint type - Final success field now checks user hard constraints in binary mode - arm_slider added to mock checker reach table (1.07m) Tests: 202 passed, 24 new tests added (optimizer 7, constraints 6, broad_phase 11) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
98 lines
2.6 KiB
Python
98 lines
2.6 KiB
Python
"""数据模型定义：Device, Lab, Placement, Constraint 及 API 请求/响应。"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass, field
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from typing import Literal
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@dataclass
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class Opening:
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"""设备的访问开口（用于方向约束）。"""
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# 设备局部坐标系中的方向单位向量，如 (0, -1) = 正前方
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direction: tuple[float, float] = (0.0, -1.0)
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label: str = ""
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@dataclass
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class Device:
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"""设备描述。"""
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id: str
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name: str
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# 碰撞包围盒 (width along X, depth along Y)，单位：米
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bbox: tuple[float, float] = (0.6, 0.4)
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device_type: Literal["static", "articulation", "rigid"] = "static"
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# 以下为可选扩展字段（向后兼容）
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height: float = 0.4
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origin_offset: tuple[float, float] = (0.0, 0.0)
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openings: list[Opening] = field(default_factory=list)
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source: Literal["registry", "assets", "manual"] = "manual"
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model_path: str = ""
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model_type: str = ""
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thumbnail_url: str = ""
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@dataclass
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class Obstacle:
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"""实验室内固定障碍物（矩形）。"""
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x: float
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y: float
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width: float
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depth: float
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@dataclass
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class Lab:
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"""实验室平面图。"""
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width: float # X 方向，单位：米
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depth: float # Y 方向，单位：米
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obstacles: list[Obstacle] = field(default_factory=list)
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@dataclass
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class Placement:
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"""单个设备的布局位姿。"""
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device_id: str
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x: float
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y: float
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theta: float # 旋转角，弧度
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uuid: str = "" # 前端分配的唯一标识，透传不生成
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def rotated_bbox(self, device: Device) -> tuple[float, float]:
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"""返回旋转后的 AABB 尺寸 (half_w, half_h)。"""
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w, d = device.bbox
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cos_t = abs(math.cos(self.theta))
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sin_t = abs(math.sin(self.theta))
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half_w = (w * cos_t + d * sin_t) / 2
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half_h = (w * sin_t + d * cos_t) / 2
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return half_w, half_h
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@dataclass
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class Constraint:
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"""约束规则。"""
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type: Literal["hard", "soft"]
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rule_name: str
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# 规则参数，含义因 rule_name 而异
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params: dict = field(default_factory=dict)
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# 仅 soft 约束使用
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weight: float = 1.0
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# 优先级等级，影响有效权重的乘数
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priority: str | None = None # "critical" | "high" | "normal" | "low"
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@dataclass
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class Intent:
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"""LLM 可生成的语义化意图，由 interpreter 翻译为 Constraint 列表。"""
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intent: str # 意图类型，如 "reachable_by", "close_together"
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params: dict = field(default_factory=dict)
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description: str = "" # 可选的自然语言描述（用于审计/调试）
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