Constrained Sampling-Based MPC Using Path Integral for Collision-Free Robot Manipulation

Abstract

The dynamic and unknown human behaviors in human–robot interaction make it challenging for collision-free robot manipulation. Although sampling-based model predictive control (MPC) has achieved real-time control in the above scenarios, it is hard to handle equality hard constraints, such as working along a specified trajectory, due to sampling disturbances. To improve manipulation performance under multiple constraints, this article presents a novel constrained sampling-based MPC (CSMPC) method using path integral. First, hierarchical optimization combining policy sampling projection and the Lagrange multiplier method is used to handle equality hard constraints for high-precision manipulation tasks. Second, collision avoidance and smooth motion are modeled as inequality soft constraints, where collision detection and time series prediction are used to ensure the safety and smoothness of dynamic interaction. Finally, an adaptive noise method is built to improve the stability of physical robot manipulation. The simulation and experiment results demonstrate that the proposed method enables a 7-DOF robot manipulator to achieve precise manipulation while avoiding dynamic obstacles.