Behavioral control task supervisor with memory based on reinforcement learning for human—multi-robot coordination systems

• Published in: Frontiers of information technology & electronic engineering Vol. 23; no. 8; pp. 1174 - 1188
• Main Authors: Huang, Jie, Mo, Zhibin, Zhang, Zhenyi, Chen, Yutao
• Format: Journal Article
• Language: English
• Published: Hangzhou Zhejiang University Press 01.08.2022; Springer Nature B.V; G+Industrial Internet Institute,Fuzhou University,Fuzhou 350108,China; School of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China; Key Laboratory of Industrial Automation Control Technology and Information Processing of Fujian Province,Fuzhou University,Fuzhou 350108,China
• Subjects: Cognitive tasks; Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Control tasks; Coordination; Effectiveness; Electrical Engineering; Electronics and Microelectronics; Instrumentation; Knowledge bases (artificial intelligence); Multiple robots; Networks; Research Article; Robots; Supervisors; Tracking errors; Human-multi-robot coordination systems; 知识库; Task supervisor; TP18; 任务管理器; Null-space-based behavioral control; Reinforcement learning; Knowledge base; 强化学习; 基于零空间行为控制; 人—多机器人协同系统
• ISSN: 2095-9184; 2095-9230
• DOI: 10.1631/FITEE.2100280

In this study, a novel reinforcement learning task supervisor (RLTS) with memory in a behavioral control framework is proposed for human—multi-robot coordination systems (HMRCSs). Existing HMRCSs suffer from high decision-making time cost and large task tracking errors caused by repeated human intervention, which restricts the autonomy of multi-robot systems (MRSs). Moreover, existing task supervisors in the null-space-based behavioral control (NSBC) framework need to formulate many priority-switching rules manually, which makes it difficult to realize an optimal behavioral priority adjustment strategy in the case of multiple robots and multiple tasks. The proposed RLTS with memory provides a detailed integration of the deep Q-network (DQN) and long short-term memory (LSTM) knowledge base within the NSBC framework, to achieve an optimal behavioral priority adjustment strategy in the presence of task conflict and to reduce the frequency of human intervention. Specifically, the proposed RLTS with memory begins by memorizing human intervention history when the robot systems are not confident in emergencies, and then reloads the history information when encountering the same situation that has been tackled by humans previously. Simulation results demonstrate the effectiveness of the proposed RLTS. Finally, an experiment using a group of mobile robots subject to external noise and disturbances validates the effectiveness of the proposed RLTS with memory in uncertain real-world environments.