fix straight endpoint collisions

examples/07_large_scale_routing.py

@@ -15,7 +15,7 @@ def main() -> None:
     # 1. Setup Environment
     bounds = (0, 0, 1000, 1000)
     engine = CollisionEngine(clearance=6.0)
-    
+
     # Bottleneck at x=500, 200um gap
     obstacles = [
         box(450, 0, 550, 400),
@@ -27,7 +27,7 @@ def main() -> None:
     danger_map = DangerMap(bounds=bounds)
     danger_map.precompute(obstacles)
 
-    evaluator = CostEvaluator(engine, danger_map, greedy_h_weight=2.0, unit_length_cost=0.1, bend_penalty=100.0, sbend_penalty=200.0, congestion_penalty=20.0)
+    evaluator = CostEvaluator(engine, danger_map, greedy_h_weight=2.0, unit_length_cost=0.1, bend_penalty=100.0, sbend_penalty=400.0, congestion_penalty=20.0)
 
     router = AStarRouter(evaluator, node_limit=2000000, snap_size=5.0, bend_radii=[50.0], sbend_radii=[50.0], use_analytical_sbends=False)
     pf = PathFinder(router, evaluator, max_iterations=50, base_congestion_penalty=20.0, congestion_multiplier=1.2)
@@ -37,7 +37,7 @@ def main() -> None:
     num_nets = 10
     start_x = 50
     start_y_base = 500 - (num_nets * 10.0) / 2.0
-    
+
     end_x = 950
     end_y_base = 100
     end_y_pitch = 800.0 / (num_nets - 1)
@@ -51,18 +51,18 @@ def main() -> None:
 
     # 3. Route
     print(f"Routing {len(netlist)} nets through 200um bottleneck...")
-    
+
     iteration_stats = []
 
     def iteration_callback(idx, current_results):
         successes = sum(1 for r in current_results.values() if r.is_valid)
         total_collisions = sum(r.collisions for r in current_results.values())
         total_nodes = pf.router.metrics['nodes_expanded']
-        
+
         # Identify Hotspots
         hotspots = {}
         overlap_matrix = {} # (net_a, net_b) -> count
-        
+
         for nid, res in current_results.items():
             if res.path:
                 for comp in res.path:
@@ -77,7 +77,7 @@ def main() -> None:
                                     cx, cy = poly.centroid.x, poly.centroid.y
                                     grid_key = (int(cx/20)*20, int(cy/20)*20)
                                     hotspots[grid_key] = hotspots.get(grid_key, 0) + 1
-                                    
+
                                     # Record pair
                                     pair = tuple(sorted((nid, other_nid)))
                                     overlap_matrix[pair] = overlap_matrix.get(pair, 0) + 1
@@ -89,7 +89,7 @@ def main() -> None:
         if hotspots:
             top_hotspots = sorted(hotspots.items(), key=lambda x: x[1], reverse=True)[:3]
             print(f"  Top Hotspots: {top_hotspots}")
-        
+
         # Adaptive Greediness: Decay from 2.0 to 1.1 over 25 iterations
         new_greedy = max(1.1, 2.0 - ((idx + 1) / 25.0))
         evaluator.greedy_h_weight = new_greedy
@@ -101,13 +101,13 @@ def main() -> None:
             'Congestion': total_collisions,
             'Nodes': total_nodes
         })
-        
+
         # Save plots only for certain iterations to save time
         if idx % 20 == 0 or idx == pf.max_iterations - 1:
             # Save a plot of this iteration's result
             fig, ax = plot_routing_results(current_results, obstacles, bounds, netlist=netlist)
             plot_danger_map(danger_map, ax=ax)
-            
+
             # Overlay failures: show where they stopped
             for nid, res in current_results.items():
                 if not res.is_valid and res.path:
@@ -116,7 +116,7 @@ def main() -> None:
                     dist = abs(last_p.x - target_p.x) + abs(last_p.y - target_p.y)
                     ax.scatter(last_p.x, last_p.y, color='red', marker='x', s=100)
                     ax.text(last_p.x, last_p.y, f" {nid} (rem: {dist:.0f}um)", color='red', fontsize=8)
-            
+
             fig.savefig(f"examples/07_iteration_{idx:02d}.png")
             import matplotlib.pyplot as plt
             plt.close(fig)
@@ -126,7 +126,7 @@ def main() -> None:
                  fig_d, ax_d = plot_expansion_density(pf.accumulated_expanded_nodes, bounds)
                  fig_d.savefig(f"examples/07_iteration_{idx:02d}_density.png")
                  plt.close(fig_d)
-        
+
         pf.router.reset_metrics()
 
     import cProfile, pstats
@@ -136,7 +136,7 @@ def main() -> None:
     results = pf.route_all(netlist, net_widths, store_expanded=True, iteration_callback=iteration_callback, shuffle_nets=True, seed=42)
     t1 = time.perf_counter()
     profiler.disable()
-    
+
     # ... (rest of the code)
     stats = pstats.Stats(profiler).sort_stats('tottime')
     stats.print_stats(20)
@@ -148,10 +148,10 @@ def main() -> None:
     print("-" * 40)
     for s in iteration_stats:
         print(f"{s['Iteration']:<5} | {s['Success']:<8} | {s['Congestion']:<8} | {s['Nodes']:<10}")
-    
+
     success_count = sum(1 for res in results.values() if res.is_valid)
     print(f"\nFinal: Routed {success_count}/{len(netlist)} nets successfully.")
-    
+
     for nid, res in results.items():
         target_p = netlist[nid][1]
         if not res.is_valid:
@@ -166,10 +166,10 @@ def main() -> None:
 
     # 5. Visualize
     fig, ax = plot_routing_results(results, obstacles, bounds, netlist=netlist)
-    
+
     # Overlay Danger Map
     plot_danger_map(danger_map, ax=ax)
-    
+
     # Overlay Expanded Nodes from last routed net (as an example)
     if pf.router.last_expanded_nodes:
         print(f"Plotting {len(pf.router.last_expanded_nodes)} expanded nodes for the last net...")

inire/geometry/collision.py

@@ -326,7 +326,7 @@ class CollisionEngine:
                         t_min = max(tx_min, ty_min)
                         t_max = min(tx_max, ty_max)
                         
-                        if t_max < 0 or t_min > t_max or t_min > 1.0:
+                        if t_max <= 1e-9 or t_min > t_max or t_min >= 1.0 - 1e-9:
                             continue
                             
                         # If rectangle, slab is exact

inire/router/astar.py

@@ -256,22 +256,38 @@ class AStarRouter:
              target_dist = abs(target.x - cp.x)
              if target_dist <= max_reach and target_dist > self.config.min_straight_length:
                   straight_lengths.add(snap_search_grid(target_dist, snap))
-                  # Space for turning: target_dist - R and target_dist - 2R
+             
-                  for radius in self.config.bend_radii:
+             # Space for turning: target_dist - R and target_dist - 2R
-                       if target_dist > radius + self.config.min_straight_length:
+             for radius in self.config.bend_radii:
-                            straight_lengths.add(snap_search_grid(target_dist - radius, snap))
+                  l1 = target_dist - radius
-                       if target_dist > 2 * radius + self.config.min_straight_length:
+                  if l1 > self.config.min_straight_length:
-                            straight_lengths.add(snap_search_grid(target_dist - 2 * radius, snap))
+                       s_l1 = snap_search_grid(l1, snap)
+                       if s_l1 <= max_reach and s_l1 > 0.1:
+                            straight_lengths.add(s_l1)
+                  
+                  l2 = target_dist - 2 * radius
+                  if l2 > self.config.min_straight_length:
+                       s_l2 = snap_search_grid(l2, snap)
+                       if s_l2 <= max_reach and s_l2 > 0.1:
+                            straight_lengths.add(s_l2)
         else: # Vertical
              target_dist = abs(target.y - cp.y)
              if target_dist <= max_reach and target_dist > self.config.min_straight_length:
                   straight_lengths.add(snap_search_grid(target_dist, snap))
-                  # Space for turning: target_dist - R and target_dist - 2R
+             
-                  for radius in self.config.bend_radii:
+             # Space for turning: target_dist - R and target_dist - 2R
-                       if target_dist > radius + self.config.min_straight_length:
+             for radius in self.config.bend_radii:
-                            straight_lengths.add(snap_search_grid(target_dist - radius, snap))
+                  l1 = target_dist - radius
-                       if target_dist > 2 * radius + self.config.min_straight_length:
+                  if l1 > self.config.min_straight_length:
-                            straight_lengths.add(snap_search_grid(target_dist - 2 * radius, snap))
+                       s_l1 = snap_search_grid(l1, snap)
+                       if s_l1 <= max_reach and s_l1 > 0.1:
+                            straight_lengths.add(s_l1)
+                  
+                  l2 = target_dist - 2 * radius
+                  if l2 > self.config.min_straight_length:
+                       s_l2 = snap_search_grid(l2, snap)
+                       if s_l2 <= max_reach and s_l2 > 0.1:
+                            straight_lengths.add(s_l2)
 
         # NO standard samples here! Only milestones.