TY  - EJOU
AU  - Liu, Zhiguang 
AU  - Wang, Shilin 
AU  - Zhao, Jian 
AU  - Hao, Jianhong 
AU  - Yu, Fei 

TI  - Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 138
IS  - 1
SN  - 1526-1506

AB  - A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve human-robot cooperation performance for a curtain wall installation task. This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation. In this paper, a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task. Firstly, the physical human-robot cooperation model, including the role factor is built. Then, a reinforcement learning model that can adjust the role factor in real time is established, and a reward and action model is designed. The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s <i>Jerk</i> during the repeated installation. Finally, the roles adjustment rule established above continuously improves the comprehensive performance. Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified. The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s <i>Jerk</i>.
KW  - Human-robot cooperation; roles allocation; reinforcement learning

DO  - 10.32604/cmes.2023.029729