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>

unilabos/layout_optimizer/tests/test_e2e_pcr_pipeline.py

@@ -185,9 +185,9 @@ class TestStage3VerifyPlacements:
     def test_no_hard_constraint_violation(self):
         """Full pipeline with all intents including reachability converges cleanly.
 
-        MockReachabilityChecker uses large fallback reach for unknown arms,
-        so arm_slider reachability constraints are satisfied in mock mode.
-        When real ROS checkers replace mock, this test validates the same pipeline.
+        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()
 
@@ -197,7 +197,7 @@ class TestStage3VerifyPlacements:
             "constraints": interpret_data["constraints"],
             "workflow_edges": interpret_data["workflow_edges"],
             "run_de": True,
-            "maxiter": 50,
+            "maxiter": 100,
             "seed": 42,
         })
         data = optimize_resp.json()