计算机集成制造系统 ›› 2019, Vol. 25 ›› Issue (第7): 1767-1774.DOI: 10.13196/j.cims.2019.07.017

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基于负反馈机制的蚁群算法及其在机器人路径规划中的应用

白建龙1,陈瀚宁1,胡亚宝1,何茂伟1,梁晓丹1,PARK Dongwon2   

  1. 1.天津工业大学计算机科学与软件学院
    2.韩国培材大学游戏工程学院
  • 出版日期:2019-07-31 发布日期:2019-07-31
  • 基金资助:
    国家自然科学基金面上资助项目(61772365);天津市科技计划资助项目(17ZLZXZF00310);十三五国家重点研发计划激光制造重点专项资助项目(2017YFB1103003);韩国高等教育财团国际学术交流资助项目。

Ant colony algorithm based on negative feedback and its application on robot path planning

  • Online:2019-07-31 Published:2019-07-31
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.61772365),the Tianjin Municipal Science & Technology Plan,China(No.17ZLZXZF00310),the National Key Research and Development Plan,China(No.2017YFB1103003),and the 2017 International Academic Exchange Fund of Korean Research Consortium(NRF),Korea.

摘要: 蚁群优化算法是解决机器人路径规划问题的有效方式。首先,利用栅格法对场景进行建模,然后再利用蚁群算法寻找简单环境或复杂环境下的最优路径。针对增强的蚁群算法易陷入局部最优解的问题,本文设计了具有负反馈机制的改进的蚁群算法,并以此来解决机器人路径规划问题。该算法利用搜索的历史信息,并通过获得失败经验,指导蚁群在优化过程中探索未知空间。该算法旨在利用负反馈来改善解的多样性,从而获得最优路径。实验结果验证所提改进算法在路径规划问题上有明显优势。

关键词: 机器人路径规划, 蚁群算法, 负反馈机制, 复杂环境

Abstract: Ant colony optimization algorithm is an effective way to solve the problem of robot path planning.The grid was used to model the scene,and then the ant colony algorithm was used to find the optimal path in a simple environment or a complex environment.Aiming at the problem that the enhanced ant colony algorithms were easy to fall into the local optimal solution in complex environment,an improved ant colony algorithm with negative feedback mechanism was designed to solve the problem of robot path planning.Through the searching history information and the failure experience,the ant colony in the optimization process was guided to explore the unknown space.The proposed algorithm was designed to improve the diversity of solutions by using negative feedback to obtain the optimal path.The exam showed the obvious advantage of the proposed algorithm on path planning problem.

Key words: robot path planning, ant colony algorithm, negative feedback mechanism, complex environment

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