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/broad_phase.py

@@ -0,0 +1,66 @@
+"""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]