A Novel Multi-robot Coordination Method Based on Reinforcement Learning

Conference Paper · September 2008with5 Reads
DOI: 10.1007/978-3-540-87442-3_148 · Source: DBLP
Conference: Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, 4th International Conference on Intelligent Computing, ICIC 2008, Shanghai, China, September 15-18, 2008, Proceedings

    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.