基于势场蚁群算法的机器人全局路径规划
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摘要
研究了智能移动机器人的全局路径规划算法改进问题.结合蚁群算法的全局性与人工势场的确定性优势,提出一种势场蚁群算法.即在基本蚁群算法迭代初期,通过人工势场法影响蚂蚁的信息素量,从而提升寻找最优路径的效率.基于栅格模型,设计了算法的执行步骤.此外,分析了不同的信息素启发因子和信息素挥发系数对算法路径长度、迭代次数和收敛速度的影响.最后仿真验证了该算法优于基本蚁群算法,也得出了信息素启发因子参数选择的合理范围.
The improvement of global path planning algorithm for intelligent mobile robots is studied. Combining the global character of basic ant colony algorithm and the deterministic advantage of artificial potential field, a potential field ant colony algorithm is proposed. In the initial iteration stage of basic ant colony algorithm, artificial potential field method is considered in the construction of the pheromone, so as to improve the efficiency of finding the optimal path. Based on the grid model, the implementation steps of the algorithm are designed. In addition, the effects of different pheromone heuristic factors and pheromone volatilization coefficients on the path length, iteration times and convergence speed of the algorithm are analyzed. Finally, the simulation results show that the algorithm is superior to the basic ant colony algorithm, and the reasonable range of pheromone heuristic factor parameter is obtained.
The improvement of global path planning algorithm for intelligent mobile robots is studied. Combining the global character of basic ant colony algorithm and the deterministic advantage of artificial potential field, a potential field ant colony algorithm is proposed. In the initial iteration stage of basic ant colony algorithm, artificial potential field method is considered in the construction of the pheromone, so as to improve the efficiency of finding the optimal path. Based on the grid model, the implementation steps of the algorithm are designed. In addition, the effects of different pheromone heuristic factors and pheromone volatilization coefficients on the path length, iteration times and convergence speed of the algorithm are analyzed. Finally, the simulation results show that the algorithm is superior to the basic ant colony algorithm, and the reasonable range of pheromone heuristic factor parameter is obtained.
引文
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[18] 罗德林,吴顺祥.基于势场蚁群算法的机器人路径规划 [J].系统工程与电子技术,2010,32(6):1277-1280.LUO Delin,WU Shunxiang.Ant colony optimization with potential field heuristic for robot path planning [J].Systems Engineering and Electronics,2010,32(6):1277-1280.(in Chinese)
[19] 刘建华,杨建国,刘华平,等.基于势场蚁群算法的移动机器人全局路径规划方法 [J].农业机械学报,2015,46(9):18-27.LIU Jianhua,YANG Jianguo,LIU Huaping,et al.Robot global path planning based on ant colony optimization with artificial potential field [J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(9):18-27.(in Chinese)
[2] 霍凤财,迟金,黄梓健,等.移动机器人路径规划算法综述 [J].吉林大学学报(信息科学版),2018,36(6):639-647.HUO Fengcai,CHI Jin,HUANG Zijian,et al.Review of path planning for mobile robots [J].Journal of Jilin University (Information Science Edition),2018,36(6):639-647.(in Chinese)
[3] KHATIB O.Real-time obstacle avoidance for manipulators and mobile robots [J].The International Journal of Robotics Research,1986,5(1):90-98.
[4] 夏梁盛,严卫生.基于栅格法的移动机器人运动规划研究 [J].计算机仿真,2012,29(12):229-233.XIA Liangsheng,YAN Weisheng.Study on mobile robot motion planning based on grid method [J].Computer Simulation,2012,29(12):229-233.(in Chinese)
[5] 施磊磊,施化吉,束长波,等.改进的模糊控制算法在机器人路径规划中的应用 [J].软件导刊,2014,13(11):46-48.SHI Leilei,SHI Huaji,SHU Changbo,et al.Application of improved fuzzy control algorithms in robot path planning [J].Software Guide,2014,13(11):46-48.(in Chinese)
[6] 潘成浩,郭敏.基于松弛Dijkstra算法的移动机器人路径规划 [J].计算机与现代化,2016(11):20-24.PAN Chenghao,GUO Min.Path planning for mobile robot based on relaxed Dijkstra algorithm [J].Computer and Modernization,2016(11):20-24.(in Chinese)
[7] 来学伟.贪心算法在TSP问题中的应用 [J].许昌学院学报,2017,36(2):41-44.LAI Xuewei.Application of probabilistic algorithm in the TSP problem [J].Journal of Xuchang University,2017,36(2):41-44.(in Chinese)
[8] PERSSON S M,SHARF I.Sampling-based A* algorithm for robot path-planning [J].The International Journal of Robotics Research,2014,33(13):1683-1708.
[9] WANG Wei,DENG Hua,WU Xiaoshun.Path planning of loaded pin-jointed bar mechanisms using Rapidly-exploring Random Tree method [J].Computers and Structures,2018,209:65-73.
[10] GHATEE M,MOHADES A.Motion planning in order to optimize the length and clearance applying a Hopfield neural network [J].Expert Systems with Applications,2009,36(3 PART 1):4688-4695.
[11] FAN Chaonan,LI Shixin,GUO Rong,et al.Analysis of AGV optimal path problem in smart factory based on genetic simulated annealing algorithm [C] // 4th Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2018).Paris:Atlantis Press,2018:166-170.
[12] ALVAREZ A,CAITI A,ONKEN R.Evolutionary path planning for autonomous underwater vehicles in a variable ocean [J].IEEE Journal of Oceanic Engineering,2004,29(2):418-429.
[13] DORIGO M,MANIEZZO V,COLORNI A.Ant system:optimization by a colony of cooperating agents [J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,1996,26(1):29-41.
[14] 余胜东,吴洪涛,马金玉.应用混沌萤火虫算法的无人机航迹规划 [J].机械设计与制造,2018(11):113-116.YU Shengdong,WU Hongtao,MA Jinyu.Path planning for unmanned air vehicle based on chaotic glowworm swarm optimization [J].Machinery Design & Manufacture,2018(11):113-116.(in Chinese)
[15] 易晟.平面机器人路径规划研究 [D].长沙:中南大学,2002.YI Sheng.Study on path planning of the planar mobile robot [D].Changsha:Central South University,2002.(in Chinese)
[16] 史恩秀,陈敏敏,李俊,等.基于蚁群算法的移动机器人全局路径规划方法研究 [J].农业机械学报,2014,45(6):53-57.SHI Enxiu,CHEN Minmin,LI Jun,et al.Research on method of global path-planning for mobile robot based on ant-colony algorithm [J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(6):53-57.(in Chinese)
[17] 刘砚菊,代涛,宋建辉.改进人工势场法的路径规划算法研究 [J].沈阳理工大学学报,2017,36(1):61-65.LIU Yanju,DAI Tao,SONG Jianhui.Research of path planning algorithm based on improved artificial potential field [J].Journal of Shenyang Ligong University,2017,36(1):61-65.(in Chinese)
[18] 罗德林,吴顺祥.基于势场蚁群算法的机器人路径规划 [J].系统工程与电子技术,2010,32(6):1277-1280.LUO Delin,WU Shunxiang.Ant colony optimization with potential field heuristic for robot path planning [J].Systems Engineering and Electronics,2010,32(6):1277-1280.(in Chinese)
[19] 刘建华,杨建国,刘华平,等.基于势场蚁群算法的移动机器人全局路径规划方法 [J].农业机械学报,2015,46(9):18-27.LIU Jianhua,YANG Jianguo,LIU Huaping,et al.Robot global path planning based on ant colony optimization with artificial potential field [J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(9):18-27.(in Chinese)
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