Robotic arm control improves accuracy under moving reference frames
MR-MPCC: Model Predictive Contouring Control for Robotic Manipulator Tracking under Moving Reference Frames
Robotics
Summary
Robotic arms need to follow precise paths even when their workspace or the path itself moves unpredictably, like in surgery or machining. The authors created a new control method called MR-MPCC that helps the robot keep on track better by focusing on the position along the path instead of just time. They tested it on a real robot arm and found it tracked paths more accurately compared to older control methods when the reference frame moved. This means robots can handle complex moving environments more reliably.
What this means in practice
- •For industrial automation engineers: Improve robotic arm accuracy during machining tasks where the workpiece or reference frame moves unexpectedly.
- •For surgical robot developers: Enhance end-effector path tracking precision under motions of the surgical site or reference frame.
Authors
Chonghao Cheng, Tiancheng Li, Jonathan Woolfrey, Liang Zhao, Shoudong Huang
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.