AIRR  >> Vol. 4 No. 2 (May 2015)

    A Multi-Robot Path Planning Method under Narrow Environments

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邵 杰,于景茹:郑州成功财经学院信息工程系,河南 郑州

路径规划多机器人学习分类器遗传算法Q学习Path Planning Multi-Robot Learning Classifier System Genetic Algorithm Q Learning



Under narrow environments, conflict easily occurs when multi-robot path planning uses shared resources, and prioritisation is an important technology to solve this problem. This paper pre- sents a dynamic allocation priority method based on learning classifier to improve the perfor-mance of the robot team. Firstly robots optimize their behaviors by XCS, and then high-level robot managers are introduced and trained to resolve conflicts by assigning priority. The novel approach is designed for partially observable Markov decision process environments. Simulation results show that the method presented is effective to solve the conflict in multi-robot path planning and improves the capacity of multi-robot path planning.

邵杰, 于景茹. 狭隘环境下一种多机器人路径规划方法[J]. 人工智能与机器人研究, 2015, 4(2): 9-16.


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