AIRR  >> Vol. 4 No. 2 (May 2015)

    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

  • 全文下载: PDF(536KB) HTML   XML   PP.9-16   DOI: 10.12677/AIRR.2015.42002  
  • 下载量: 839  浏览量: 3,257   科研立项经费支持

作者:  

邵 杰,于景茹:郑州成功财经学院信息工程系,河南 郑州

关键词:
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

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.

文章引用:
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. http://dx.doi.org/10.12677/AIRR.2015.42002

参考文献

[1] Masouri, M. and Aliyari, M. (2008) Teshnehlab. Integer GA for mobile robot path planning with using another GA as repairing function. International Conference on Automation and Logistics, Qingdao, 1-3 September 2008, 135-140.
[2] 孟伟, 黄庆成, 韩学东, 等 (2005) 一种动态未知环境中自主机器人的导航方法. 计算机研究与发展, 9, 1538- 1543.
[3] 邵杰, 杨静宇, 万鸣华, 黄传波 (2010) 基于学习分类器的多机器人路径规划收敛性研究. 计算机研究与发展, 5, 948-955.
[4] 孟偲, 王田苗 (2008) 一种移动机器人全局最优路径规划算法. 机器人, 3, 217-222.
[5] Gao, Y. and Sun, S.-D. (2009) A collision based local path planning of mobile robot. 2009 International Asia Conference on Informatics in Control, Automation and Robots, Piscataway, 2009, 185-190.
[6] 曲道奎, 杜振军, 徐殿国, 等 (2008) 移动机器人路径规划方法研究. 机器人, 2, 97-106.
[7] Holland, J.H. (1986) A mathematical frame work for studying learning in classifier systems. In: Farmer, D., Lapedes, A., Packard, N. and Wendroff, B., Eds., Evolution, Games and Learning, North-Holland, Amsterdam, 307-317.
[8] Musilek, P. (2005) Enhanced learning classifier system for robot navigatio. Intelligent Robots and Systems 2005 International Conference, Piscataway, 2005, 3390-3395.
[9] Larry, B., Mattew, S., Anthony, B., et al. (2007) Learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11, 496-502.
[10] 邵杰, 杨静宇 (2011) 基于多LCS和人工势场法的机器人行为控制. 计算机科学, 1, 948-951.