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

作　　者：邵杰[1] 于景茹[1] 

机构地区：[1]郑州成功财经学院信息工程系,河南郑州

出　　处：《人工智能与机器人研究》2015年第2期9-16,共8页Artificial Intelligence and Robotics Research

基　　金：2013河南省高等学校“专业综合改革试点”项目;河南省教育厅重点基金资助项目(12B520047)。

摘　　要：狭隘环境下多机器人路径规划使用共享资源时,极易产生冲突,优先顺序化是解决共享资源冲突的一个重要技术。本文提出了一种基于学习分类器的动态分配优先权的方法,提高机器人团队的性能。首先机器人通过XCS优化各自的行为,然后引入和训练高水平的机器人管理者来分配优先权解决冲突。本方法适用于部分可知的Markov环境,仿真实验结果表明本文所提方法用于解决多机器人的路径规划冲突是有效的,提高了多机器人系统解决路径规划冲突的能力。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.

关 键 词：路径规划 多机器人 学习分类器 遗传算法 Q学习 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]