optimize_trajectory
Optimize robot trajectory for time, energy, or smoothness by adjusting waypoints to improve motion efficiency and performance.
Instructions
优化轨迹（时间、能耗或平滑度）
参数:
- ip: 机器人IP地址
- waypoints: 路径点列表
- optimization_type: 优化类型，可选值包括"time", "energy", "smoothness"
返回:
- 优化后的路径
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes | ||
| waypoints | Yes | ||
| optimization_type | No | time |
Implementation Reference
- MCP tool handler for optimize_trajectory: validates input, checks connection, converts waypoints, calls AdvancedTrajectoryPlanner.optimize_trajectory, formats and returns optimized path.@mcp.tool() def optimize_trajectory(ip: str, waypoints: list, optimization_type: str = "time"): """ 优化轨迹（时间、能耗或平滑度） 参数: - ip: 机器人IP地址 - waypoints: 路径点列表 - optimization_type: 优化类型，可选值包括"time", "energy", "smoothness" 返回: - 优化后的路径 """ try: if '连接失败' in link_check(ip): return return_msg(f"与机器人的连接已断开。") if advanced_trajectory_planner is None: return return_msg("高级轨迹规划器未初始化") # 转换waypoints格式 points = [ [p["x"], p["y"], p["z"], p["rx"], p["ry"], p["rz"]] for p in waypoints ] # 获取机器人模型参数 robot_model = robotModle_list[ip] # 优化轨迹 optimized_path = advanced_trajectory_planner.optimize_trajectory( waypoints=points, optimization_type=optimization_type, robot_model=robot_model ) # 转换回字典格式 result_path = [] for point in optimized_path: result_path.append({ "x": point[0], "y": point[1], "z": point[2], "rx": point[3], "ry": point[4], "rz": point[5] }) return return_msg({"optimized_path": result_path}) except Exception as e: logger.error(f"优化轨迹失败: {str(e)}") return return_msg(f"优化轨迹失败: {str(e)}")
- src/nonead_universal_robots_mcp/server.py:965-965 (registration)FastMCP tool registration decorator for optimize_trajectory.@mcp.tool()
- Function signature with type hints and docstring defining input schema (ip: str, waypoints: list of dicts with x,y,z,rx,ry,rz, optimization_type: str default 'time') and output (dict with optimized_path).def optimize_trajectory(ip: str, waypoints: list, optimization_type: str = "time"): """ 优化轨迹（时间、能耗或平滑度） 参数: - ip: 机器人IP地址 - waypoints: 路径点列表 - optimization_type: 优化类型，可选值包括"time", "energy", "smoothness" 返回: - 优化后的路径 """
- Initializes the global advanced_trajectory_planner instance used by the optimize_trajectory tool.def initialize_extended_modules(): """初始化所有扩展模块""" global multi_robot_coordinator, advanced_trajectory_planner global advanced_data_recorder, advanced_data_analyzer try: # 初始化多机器人协调器 multi_robot_coordinator = AdvancedMultiRobotCoordinator() logger.info("高级多机器人协调器初始化成功") # 初始化高级轨迹规划器 advanced_trajectory_planner = AdvancedTrajectoryPlanner() logger.info("高级轨迹规划器初始化成功") # 初始化高级数据记录器 advanced_data_recorder = AdvancedDataRecorder() logger.info("高级数据记录器初始化成功") # 获取数据分析器实例（单例） advanced_data_analyzer = get_data_analyzer() logger.info("高级数据分析器初始化成功") return True except Exception as e: logger.error(f"初始化扩展模块失败: {str(e)}") return False
- Delegation to AdvancedTrajectoryPlanner.optimize_trajectory for core optimization logic.optimized_path = advanced_trajectory_planner.optimize_trajectory( waypoints=points, optimization_type=optimization_type, robot_model=robot_model ) # 转换回字典格式 result_path = [] for point in optimized_path: result_path.append({ "x": point[0], "y": point[1], "z": point[2], "rx": point[3], "ry": point[4], "rz": point[5] }) return return_msg({"optimized_path": result_path}) except Exception as e: logger.error(f"优化轨迹失败: {str(e)}") return return_msg(f"优化轨迹失败: {str(e)}")