外观
论文arXiv 2610.2129213 天前
MR-MPCC:移动参考系下机器人机械臂跟踪的模型预测轮廓控制
原文链接:MR-MPCC: Model Predictive Contouring Control for Robotic Manipulator Tracking under Moving Reference Frames(arXiv 2610.21292 · 发布日期:2026-10-15)
MR-MPCC: Model Predictive Contouring Control for Robotic Manipulator Tracking under Moving Reference Frames
提出MR-MPCC以降低移动参考系下机械臂路径跟踪误差,并在ROS2--MuJoCo与UR5e平台验证。
核心要点
- 面向加工和手术自动化等高精度任务,移动参考系会导致路径相对误差累积。
- 采用分层架构,上层MPCC优化路径进度并惩罚轮廓与姿态误差。
- 下层受约束RMRC将笛卡尔速度指令映射为满足关节与安全约束的关节速度。
- 在移动参考扰动下,相比PI控制和时间索引跟踪MPC获得更低几何路径误差。
验证范围与局限:验证包括ROS2--MuJoCo流程和UR5e平台,任务为路径框架受扰并由电磁传感器跟踪的移动参考扰动场景。
英文摘要 (Abstract)
Maintaining small path-tracking errors under moving reference frames is essential for robotic manipulation tasks with tight execution tolerances, such as machining and surgical automation. Conventional time-indexed controllers, including tracking Model Predictive Control (MPC), rely on a fixed temporal correspondence with the reference trajectory and may therefore accumulate path-relative error under abrupt reference-frame motion or timing mismatch. This paper presents MR-MPCC, a Model Predictive Contouring Control (MPCC) framework for velocity-controlled robotic manipulators with 6-DoF end-effector pose regulation under Moving-Reference disturbances. A hierarchical control architecture is adopted, in which an upper-level MPCC optimizes path progress while penalizing contouring and orientation errors, and a constrained resolved motion rate control (RMRC) layer maps the commanded Cartesian twist to feasible joint velocities under joint-level and safety constraints. MR-MPCC is implemented in a ROS2--MuJoCo pipeline and validated on a UR5e platform under moving-reference disturbances, where the path frame is perturbed and tracked by electromagnetic sensors. Compared with PI control and time-indexed tracking MPC under matched disturbance, MR-MPCC achieves lower geometric path-following error and smaller deviations in the moving path frame, demonstrating improved path-following performance under reference-frame motion.
作者:Chonghao Cheng, Tiancheng Li, Jonathan Woolfrey, Liang Zhao, Shoudong Huang
来源
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