A Novel Multi-robot Coordination Method Based on Reinforcement Learning

Authors:
Jian Fan;Minrui Fei;Likang Shao;Feng Huang
Affiliations:
Shanghai Key Laboratory of Power Station Automation Technology, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China 200072 and Operations Research Centre, Nanj ...;Shanghai Key Laboratory of Power Station Automation Technology, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China 200072;PLA Artillery Academy, HeFei, China 230031;Staff Room of People's Air Defence Command and Communications, PLA Institute of Communication and Command, Wuhan, china 430010
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

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Abstract

Focusing on multi-robot coordination, role transformation and reinforcement learning method are combined in this paper. Under centralize control framework, the distance nearest rule which means that the nearest robot ranges from obstacles is selected to be the master robot for controlling salve robots is presented. Meanwhile, different from traditional way which reinforcement learning is applied in online learning of multi-robot coordination, this paper proposed a novel behavior weight method based on reinforcement learning, the robot behavior weights are optimized through interacting with environment and the coordination policy based on maximum behavior value is presented to plan the collision avoidance behavior of robot. The learning method proposed in this paper is applied to the application related to collaboration movement of mobile robots and demonstrated by the simulation results presented in this paper.