feat(layout_optimizer): DE optimizer V2 — custom loop, graduated hard constraints, broad phase

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>
This commit is contained in:
yexiaozhou
2026-04-01 00:32:34 +08:00
parent 64eeed56a1
commit 9ef24b7768
12 changed files with 1072 additions and 83 deletions

View File

@@ -90,9 +90,16 @@ class TestDuplicateDeviceIDs:
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 math.isinf(cost)
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_uuid(self):
"""create_devices_from_list should use uuid as Device.id."""