Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

作     者：Chao ZENG Chenguang YANG Zhaopeng CHEN Chao ZENG;Chenguang YANG;Zhaopeng CHEN

作者机构：School of Automation Science and Engineering South China University of Technology TAMS Group Informatics University of Hamburg 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2020年第63卷第7期

页      面：69-78页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61861136009  61811530281) 

主　　题：impedance learning biomimetic control human-robot collaboration 

摘      要：To improve the robotic flexibility and dexterity in a human-robot collaboration task, it is important to adapt the robot impedance in a real-time manner to its partner’s behavior. However, it is often quite challenging to achieve this goal and has not been well addressed yet. In this paper, we propose a bio-inspired approach as a possible solution, which enables the online adaptation of robotic impedance in the unknown and dynamic environment. Specifically, the bio-inspired mechanism is derived from the human motor learning, and it can automatically adapt the robotic impedance and feedforward torque along the motion trajectory. It can enable the learning of compliant robotic behaviors to meet the dynamic requirements of the interactions. In order to validate the proposed approach, an experiment containing an anti-disturbance test and a human-robot collaborative sawing task has been conducted.