feat: 优化路径规划轨迹贴合度，确保末端精确跟踪

核心改进：
- 限制shortcut优化距离（0.3→0.15），减少迭代次数（50→5）
- 新增路径密集化功能，确保关节间距≤0.05弧度
- 在_simplify_path中添加距离限制，防止过度优化
- 添加_densify_path方法保证轨迹安全性

技术成果：
- 路径点从6个增加到24个，最大关节间距从0.1166降至0.0254
- 确保机械臂末端严格沿规划路径移动，解决轨迹不可控问题
- 支持不同自由度机械臂，遵循配置驱动原则

测试验证：
- 新增test_path_improvement.py演示改进效果
- GUI可视化对比原始路径和优化路径
- 实时机械臂运动验证轨迹贴合度

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

src/planning/path_optimizer.py

@@ -12,7 +12,9 @@ import numpy as np
 from typing import List, Tuple, Optional, Dict, Any
 
 # 路径优化参数
-SHORTCUT_ITERATIONS = 50
+SHORTCUT_ITERATIONS = 5
+MAX_SHORTCUT_DISTANCE = 0.15
+DENSIFICATION_STEP = 0.05
 SMOOTHING_FACTOR = 0.5
 
 
@@ -32,6 +34,8 @@ class PathOptimizer:
         
         # 优化参数（使用文件内常量）
         self.shortcut_iterations = SHORTCUT_ITERATIONS
+        self.max_shortcut_distance = MAX_SHORTCUT_DISTANCE
+        self.densification_step = DENSIFICATION_STEP
         self.smoothing_factor = SMOOTHING_FACTOR
         
         # 从配置读取跨文件共享参数
@@ -70,11 +74,14 @@ class PathOptimizer:
         # 步骤1: 路径简化（移除冗余点）
         simplified = self._simplify_path(path, collision_checker)
         
-        # 步骤2: 捷径优化
+        # 步骤2: 捷径优化（限制距离）
         shortcut = self._shortcut_path(simplified, collision_checker)
         
-        # 步骤3: 路径平滑
+        # 步骤3: 路径密集化（保证轨迹贴合）
-        smoothed = self._smooth_path(shortcut, collision_checker)
+        dense = self._densify_path(shortcut, collision_checker)
+        
+        # 步骤4: 路径平滑
+        smoothed = self._smooth_path(dense, collision_checker)
         
         return smoothed
     
@@ -100,6 +107,13 @@ class PathOptimizer:
             farthest_idx = current_idx + 1
             
             for idx in range(current_idx + 2, len(path)):
+                # 检查距离限制
+                distance = np.linalg.norm(
+                    np.array(path[idx]) - np.array(path[current_idx])
+                )
+                if distance > self.max_shortcut_distance:
+                    break
+                
                 # 检查直接连接是否无碰撞
                 if self._is_edge_collision_free(
                     path[current_idx], 
@@ -136,6 +150,13 @@ class PathOptimizer:
             i = np.random.randint(0, len(optimized) - 2)
             j = np.random.randint(i + 2, len(optimized))
             
+            # 检查距离限制
+            distance = np.linalg.norm(
+                np.array(optimized[j]) - np.array(optimized[i])
+            )
+            if distance > self.max_shortcut_distance:
+                continue
+            
             # 尝试直接连接
             if self._is_edge_collision_free(
                 optimized[i], 
@@ -147,6 +168,47 @@ class PathOptimizer:
         
         return optimized
     
+    def _densify_path(self, path: List[List[float]], collision_checker) -> List[List[float]]:
+        """
+        密集化路径，确保相邻点间距小于阈值
+        
+        Args:
+            path: 输入路径
+            collision_checker: 碰撞检测器
+            
+        Returns:
+            密集化后的路径
+        """
+        if len(path) <= 2:
+            return path
+        
+        densified = [path[0]]
+        
+        for i in range(len(path) - 1):
+            current = np.array(path[i])
+            next_point = np.array(path[i + 1])
+            
+            # 计算两点间距离
+            distance = np.linalg.norm(next_point - current)
+            
+            # 如果距离超过阈值，插入中间点
+            if distance > self.densification_step:
+                num_segments = int(np.ceil(distance / self.densification_step))
+                
+                for j in range(1, num_segments):
+                    ratio = j / num_segments
+                    interpolated = current + ratio * (next_point - current)
+                    
+                    # 检查插值点是否有碰撞
+                    if not collision_checker.check_collision(interpolated.tolist()):
+                        densified.append(interpolated.tolist())
+                    else:
+                        raise RuntimeError(f"Densification created collision at segment {i}")
+            
+            densified.append(path[i + 1])
+        
+        return densified
+    
     def _smooth_path(self, path: List[List[float]], collision_checker) -> List[List[float]]:
         """
         平滑路径

test_path_improvement.py

@@ -0,0 +1,258 @@
+#!/usr/bin/env python3
+"""
+测试路径优化改进效果
+验证密集采样是否能保证轨迹贴合度
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from src.config_loader import ConfigLoader
+from src.robot.arm_controller import create_arm_controller
+from src.simulation.environment import Environment
+from src.planning.ai_rrt_star import AIRRTStarPlanner
+from src.planning.collision_checker import CollisionChecker
+from src.planning.path_optimizer import PathOptimizer
+import pybullet as p
+import pybullet_data
+import numpy as np
+import time
+
+def test_path_improvement():
+    """测试路径优化改进"""
+    print("=== 测试路径优化改进 ===")
+    
+    # 初始化仿真（显示GUI）
+    physics_client = p.connect(p.GUI)
+    p.setAdditionalSearchPath(pybullet_data.getDataPath())
+    p.setGravity(0, 0, 0)
+    
+    try:
+        # 加载配置和组件
+        config_loader = ConfigLoader()
+        arm_controller = create_arm_controller(config_loader, physics_client)
+        environment = Environment(config_loader, physics_client)
+        collision_checker = CollisionChecker(arm_controller, environment, config_loader)
+        path_optimizer = PathOptimizer(arm_controller, config_loader)
+        
+        print(f"优化参数设置:")
+        print(f"  SHORTCUT_ITERATIONS: {path_optimizer.shortcut_iterations}")
+        print(f"  MAX_SHORTCUT_DISTANCE: {path_optimizer.max_shortcut_distance}")
+        print(f"  DENSIFICATION_STEP: {path_optimizer.densification_step}")
+        
+        # 创建一个简单的测试路径
+        current_joints = arm_controller.get_current_joint_positions()
+        target_joints = current_joints.copy()
+        target_joints[0] += 0.5  # 第一个关节转动0.5弧度
+        target_joints[1] += 0.3  # 第二个关节转动0.3弧度
+        
+        # 检查起止点距离
+        start_end_distance = np.linalg.norm(np.array(target_joints) - np.array(current_joints))
+        print(f"起止点关节空间距离: {start_end_distance:.4f}")
+        print(f"Shortcut距离限制: {path_optimizer.max_shortcut_distance}")
+        
+        # 设置更好的视角
+        p.resetDebugVisualizerCamera(
+            cameraDistance=4.0,
+            cameraYaw=45,
+            cameraPitch=-30,
+            cameraTargetPosition=[0, 0, 0],
+            physicsClientId=physics_client
+        )
+        
+        # 创建包含5个中间点的路径
+        original_path = []
+        for i in range(6):  # 6个点：起点+4个中间点+终点
+            ratio = i / 5.0
+            interpolated = np.array(current_joints) * (1 - ratio) + np.array(target_joints) * ratio
+            original_path.append(interpolated.tolist())
+        
+        print(f"\n原始路径: {len(original_path)} 个点")
+        
+        # 优化路径
+        optimized_path = path_optimizer.optimize_path(original_path, collision_checker)
+        print(f"优化后路径: {len(optimized_path)} 个点")
+        
+        # 计算轨迹贴合度
+        print("\n=== 轨迹贴合度分析 ===")
+        
+        # 计算原始路径的笛卡尔轨迹
+        original_cartesian = []
+        for config in original_path:
+            pos, _ = arm_controller.forward_kinematics(config)
+            original_cartesian.append(pos)
+        
+        # 计算优化路径的笛卡尔轨迹
+        optimized_cartesian = []
+        for config in optimized_path:
+            pos, _ = arm_controller.forward_kinematics(config)
+            optimized_cartesian.append(pos)
+        
+        # 计算关节空间路径长度
+        def calculate_joint_path_length(path):
+            length = 0
+            for i in range(len(path) - 1):
+                length += np.linalg.norm(np.array(path[i+1]) - np.array(path[i]))
+            return length
+        
+        # 计算笛卡尔空间路径长度
+        def calculate_cartesian_path_length(path):
+            length = 0
+            for i in range(len(path) - 1):
+                length += np.linalg.norm(np.array(path[i+1]) - np.array(path[i]))
+            return length
+        
+        original_joint_length = calculate_joint_path_length(original_path)
+        optimized_joint_length = calculate_joint_path_length(optimized_path)
+        
+        original_cart_length = calculate_cartesian_path_length(original_cartesian)
+        optimized_cart_length = calculate_cartesian_path_length(optimized_cartesian)
+        
+        print(f"原始路径 - 关节空间长度: {original_joint_length:.4f}")
+        print(f"优化路径 - 关节空间长度: {optimized_joint_length:.4f}")
+        print(f"原始路径 - 笛卡尔长度: {original_cart_length:.4f}")
+        print(f"优化路径 - 笛卡尔长度: {optimized_cart_length:.4f}")
+        
+        # 计算最大关节间距
+        def max_joint_distance(path):
+            max_dist = 0
+            for i in range(len(path) - 1):
+                dist = np.linalg.norm(np.array(path[i+1]) - np.array(path[i]))
+                max_dist = max(max_dist, dist)
+            return max_dist
+        
+        max_original_dist = max_joint_distance(original_path)
+        max_optimized_dist = max_joint_distance(optimized_path)
+        
+        print(f"原始路径最大关节间距: {max_original_dist:.4f}")
+        print(f"优化路径最大关节间距: {max_optimized_dist:.4f}")
+        
+        # 验证密集化效果
+        densification_threshold = path_optimizer.densification_step
+        if max_optimized_dist <= densification_threshold:
+            print(f"✅ 密集化成功：最大间距 {max_optimized_dist:.4f} <= 阈值 {densification_threshold}")
+        else:
+            print(f"⚠️ 密集化部分成功：最大间距 {max_optimized_dist:.4f} > 阈值 {densification_threshold}")
+        
+        # 验证shortcut限制效果
+        print(f"\n=== Shortcut限制验证 ===")
+        shortcut_threshold = path_optimizer.max_shortcut_distance
+        print(f"Shortcut距离限制: {shortcut_threshold}")
+        
+        if len(optimized_path) >= len(original_path) * 0.5:  # 保留了至少50%的点
+            print("✅ Shortcut优化受到限制，保留了足够的中间点")
+        else:
+            print("⚠️ Shortcut优化过度，可能影响轨迹贴合")
+        
+        # 可视化路径对比
+        print(f"\n=== 路径可视化 ===")
+        print("绘制路径轨迹...")
+        
+        # 绘制原始路径（蓝色）
+        original_line_ids = []
+        for i in range(len(original_cartesian) - 1):
+            line_id = p.addUserDebugLine(
+                original_cartesian[i], 
+                original_cartesian[i + 1],
+                lineColorRGB=[0, 0, 1],  # 蓝色 - 原始路径
+                lineWidth=2,
+                physicsClientId=physics_client
+            )
+            original_line_ids.append(line_id)
+        
+        # 绘制优化后路径（红色）
+        optimized_line_ids = []
+        for i in range(len(optimized_cartesian) - 1):
+            line_id = p.addUserDebugLine(
+                optimized_cartesian[i], 
+                optimized_cartesian[i + 1],
+                lineColorRGB=[1, 0, 0],  # 红色 - 优化路径
+                lineWidth=3,
+                physicsClientId=physics_client
+            )
+            optimized_line_ids.append(line_id)
+        
+        # 标记起止点
+        start_marker = p.addUserDebugLine(
+            original_cartesian[0], 
+            [original_cartesian[0][0], original_cartesian[0][1], original_cartesian[0][2] + 0.2],
+            lineColorRGB=[0, 1, 0],  # 绿色 - 起点
+            lineWidth=5,
+            physicsClientId=physics_client
+        )
+        
+        end_marker = p.addUserDebugLine(
+            original_cartesian[-1], 
+            [original_cartesian[-1][0], original_cartesian[-1][1], original_cartesian[-1][2] + 0.2],
+            lineColorRGB=[0, 1, 1],  # 青色 - 终点
+            lineWidth=5,
+            physicsClientId=physics_client
+        )
+        
+        print("可视化说明:")
+        print("  蓝色线条 = 原始路径")
+        print("  红色线条 = 优化后路径")
+        print("  绿色标记 = 起点")
+        print("  青色标记 = 终点")
+        
+        # 演示机械臂运动
+        print(f"\n=== 机械臂运动演示 ===")
+        print("开始执行优化后的路径...")
+        
+        # 设置机械臂到初始位置
+        arm_controller.set_joint_positions(optimized_path[0])
+        for _ in range(30):  # 等待稳定
+            p.stepSimulation(physicsClientId=physics_client)
+            time.sleep(0.01)
+        
+        # 逐步执行路径
+        for i, config in enumerate(optimized_path):
+            print(f"移动到waypoint {i+1}/{len(optimized_path)}")
+            arm_controller.set_joint_positions(config)
+            
+            # 逐步移动，显示中间过程
+            for _ in range(20):  # 每个waypoint停留0.2秒
+                p.stepSimulation(physicsClientId=physics_client)
+                time.sleep(0.01)
+        
+        print("路径执行完成！")
+        
+        print("\n=== 测试总结 ===")
+        improvements = []
+        if max_optimized_dist <= densification_threshold:
+            improvements.append("密集化确保关节间距合理")
+        if len(optimized_path) >= len(original_path) * 0.5:
+            improvements.append("Shortcut优化受到限制")
+        
+        if improvements:
+            print("✅ 改进效果:")
+            for improvement in improvements:
+                print(f"  - {improvement}")
+        else:
+            print("❌ 改进效果不明显，需要进一步调整参数")
+        
+        print(f"\n仿真将保持开启30秒，请观察路径可视化效果...")
+        print("如需提前关闭，请关闭PyBullet窗口")
+        
+        for i in range(30):
+            time.sleep(1)
+            # 检查是否还连接
+            try:
+                p.getConnectionInfo(physicsClientId=physics_client)
+            except:
+                print("仿真窗口已关闭")
+                break
+            if i % 5 == 0:
+                print(f"剩余 {30-i} 秒...")
+        
+    except Exception as e:
+        print(f"测试过程中发生错误: {e}")
+        import traceback
+        traceback.print_exc()
+        
+    finally:
+        p.disconnect(physicsClientId=physics_client)
+
+if __name__ == "__main__":
+    test_path_improvement()