Hybrid Trajectory Replanning-Based Dynamic Obstacle Avoidance for Physical Human-Robot Interaction

• DOI: 10.1007/s10846-021-01510-2
• Publication Date: 2021-10-13T13:19:19Z
• Authors: Shiqi Li; Ke Han; Xiao Li; Shuai Zhang; Youjun Xiong; Zheng Xie
• Keywords: 0209 industrial biotechnology; 02 engineering and technology

This paper presents a hierarchical replanning framework that rapidly modulates the ongoing trajectory to help 7-DoF redundant manipulators avoid dynamic obstacles in physical human-robot interaction. In this method, the safety requirements of the end-effector and links are met without adding additional control burden. Firstly, an improved rapidly-exploring random trees planner is utilized to initialize the path in the joint space. To prevent excessive trimming, a heuristic method based on geometric path descriptors is proposed for finding and removing redundant nodes. Secondly, a hybrid scheme combining local path rewiring and redundancy-based node self-motion is used to identify and replan the part of the pre-planned path affected by dynamic obstacles. Lastly, an adaptive online trajectory generator is designed to adjust the trajectory in real time and generate smooth motion primitives for actuators. Simulations and real-world experiments validate the effectiveness and robustness of the proposed framework for whole-arm obstacle avoidance in dynamic scenarios. Compared to other methods, our approach has a 35% higher success rate and a 47.2% reduction in replanning time.