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狭隘环境下一种多机器人路径规划方法A Multi-Robot Path Planning Method under Narrow Environments
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
《Artificial Intelligence and Robotics Research》, Vol.4 No.2, 2015-05-29
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.