基于IAFSA和AGA混合算法的移动机器人路径规划
|
摘要
针对人工鱼群算法在移动机器人路径规划中存在易陷入局部最优、结果精度不高以及遗传算法存在易早熟、收敛速度慢等问题,提出一种改进人工鱼群算法(IAFSA)和自适应遗传算法(AGA)相融合的移动机器人路径规划方法。首先用栅格法建立移动机器人的环境模型,然后用IAFSA搜索移动机器人的初始可行路径,将搜索到的初始可行路径作为AGA的初始种群,最后采用AGA优化移动机器人的全局最优路径。仿真结果表明,混合算法在结果精度和稳定性方面优于标准人工鱼群算法,在跳出局部最优和收敛速度方面优于标准遗传算法。
The artificial fish swarm algorithm is easy to fall into local optimization,and has the problem of inaccurate result for path planning of mobile robot,and the genetic algorithm has the problems of easy prematurity and slow convergence speed for path planning. Therefore,a mobile robot path planning method based on improved artificial fish swarm algorithm(IAFSA)and adaptive genetic algorithm(AGA)is proposed. The grid method is used to establish the environment model of mobile robot,and then the IAFSA is used to search the initial feasible path of mobile robot. The searched initial feasible path is taken as the initial population of AGA. The AGA is adopted to optimize the global optimal path of mobile robot. The simulation results show that the hybrid algorithm is superior to the standard artificial fish swarm algorithm in the aspects of result accuracy and stability,and is superior to the standard genetic algorithm in the aspects of local optimization avoidance and convergence speed.
The artificial fish swarm algorithm is easy to fall into local optimization,and has the problem of inaccurate result for path planning of mobile robot,and the genetic algorithm has the problems of easy prematurity and slow convergence speed for path planning. Therefore,a mobile robot path planning method based on improved artificial fish swarm algorithm(IAFSA)and adaptive genetic algorithm(AGA)is proposed. The grid method is used to establish the environment model of mobile robot,and then the IAFSA is used to search the initial feasible path of mobile robot. The searched initial feasible path is taken as the initial population of AGA. The AGA is adopted to optimize the global optimal path of mobile robot. The simulation results show that the hybrid algorithm is superior to the standard artificial fish swarm algorithm in the aspects of result accuracy and stability,and is superior to the standard genetic algorithm in the aspects of local optimization avoidance and convergence speed.
引文
[1]潘琪.自主移动机器人的路径规划算法研究[D].长沙:湖南大学,2016.PAN Qi. Research on path planning algorithm for autonomous mobile robot[D]. Changsha:Hunan University,2016.
[2]赵开新,魏勇,徐立新,等.改进遗传算法在移动机器人路径规划中的研究[J].科技通报,2015,31(9):233-236.ZHAO Kaixin,WEI Yong,XU Lixin,et al. Research of improved ant genetic algorithm in mobile robot path planning[J].Bulletin of science and technology,2015,31(9):233-236.
[3]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.
[4]张素芹.机器人BP神经网络避障控制模型构建及仿真[J].西安工业大学学报,2015,35(8):678-682.ZHANG Suqin. Building and simulation of BP neural network in robot obstacle avoidance control model[J]. Journal of Xi’an Technological University,2015,35(8):678-682.
[5]姚正华,任子晖,陈艳娜.基于人工鱼群算法的煤矿救援机器人路径规划[J].煤矿机械,2014,35(4):59-61.YAO Zhenghua,REN Zihui,CHEN Yanna. Path planning for mine rescue robot based on AFSA[J]. Coal mine machinery,2014,35(4):59-61.
[6]李杨,战艺,李岩舟.基于改进蜘蛛群居算法的移动机器人路径规划方法研究[J].科学技术与工程,2016,35(16):225-230.LI Yang,ZHAN Yi,LI Yanzhou. Research on method of pathplanning for mobile robot based on improving social spider algorithm[J]. Science technology and engineering,2016,35(16):225-230.
[7]杨兴,张亚,杨巍,等.室内移动机器人路径规划研究[J].科学技术与工程,2016,16(15):234-238.YANG Xing,ZHANG Ya,YANG Wei,et al. Research on path planning of indoor mobile robots[J]. Science technology and enginneering,2016,16(15):234-238.
[8]徐晓晴,朱庆保.动态环境下基于多人工鱼群算法和避碰规则库的机器人路径规划[J].电子学报,2012,40(8):1694-1700.XU Xiaoqing,ZHU Qingbao. Multi-artificial fish-swarm algorithm and a rule library based dynamic collision avoidance algorithm for robot path planning in a dynamic environment[J]. Acta eletronica Sinica,2012,40(8):1694-1700.
[9] WANG Jian,WU Lijuan. Robot path planning based on artificial fish swarm algorithm under a known environment[J]. Advanced materials research,2014,989:2467-2469.
[10]张文辉,林子安,刘彤,等.基于改进人工鱼群算法的机器人路径规划[J].计算机仿真,2016,33(12):374-379.ZHANG Wenhui,LIN Zian,LIU Tong,et al. Robot path planning method based on modified artificial fish swarm algorithm[J]. Computer simulation,2016,33(12):374-379.
[11] VASU V,JYOTHI KUMAR K. Optimal path planning of an autonomous mobile robot using genetic algorithm[J]. Advanced materials research,2012,488:1747-1751.
[2]赵开新,魏勇,徐立新,等.改进遗传算法在移动机器人路径规划中的研究[J].科技通报,2015,31(9):233-236.ZHAO Kaixin,WEI Yong,XU Lixin,et al. Research of improved ant genetic algorithm in mobile robot path planning[J].Bulletin of science and technology,2015,31(9):233-236.
[3]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.
[4]张素芹.机器人BP神经网络避障控制模型构建及仿真[J].西安工业大学学报,2015,35(8):678-682.ZHANG Suqin. Building and simulation of BP neural network in robot obstacle avoidance control model[J]. Journal of Xi’an Technological University,2015,35(8):678-682.
[5]姚正华,任子晖,陈艳娜.基于人工鱼群算法的煤矿救援机器人路径规划[J].煤矿机械,2014,35(4):59-61.YAO Zhenghua,REN Zihui,CHEN Yanna. Path planning for mine rescue robot based on AFSA[J]. Coal mine machinery,2014,35(4):59-61.
[6]李杨,战艺,李岩舟.基于改进蜘蛛群居算法的移动机器人路径规划方法研究[J].科学技术与工程,2016,35(16):225-230.LI Yang,ZHAN Yi,LI Yanzhou. Research on method of pathplanning for mobile robot based on improving social spider algorithm[J]. Science technology and engineering,2016,35(16):225-230.
[7]杨兴,张亚,杨巍,等.室内移动机器人路径规划研究[J].科学技术与工程,2016,16(15):234-238.YANG Xing,ZHANG Ya,YANG Wei,et al. Research on path planning of indoor mobile robots[J]. Science technology and enginneering,2016,16(15):234-238.
[8]徐晓晴,朱庆保.动态环境下基于多人工鱼群算法和避碰规则库的机器人路径规划[J].电子学报,2012,40(8):1694-1700.XU Xiaoqing,ZHU Qingbao. Multi-artificial fish-swarm algorithm and a rule library based dynamic collision avoidance algorithm for robot path planning in a dynamic environment[J]. Acta eletronica Sinica,2012,40(8):1694-1700.
[9] WANG Jian,WU Lijuan. Robot path planning based on artificial fish swarm algorithm under a known environment[J]. Advanced materials research,2014,989:2467-2469.
[10]张文辉,林子安,刘彤,等.基于改进人工鱼群算法的机器人路径规划[J].计算机仿真,2016,33(12):374-379.ZHANG Wenhui,LIN Zian,LIU Tong,et al. Robot path planning method based on modified artificial fish swarm algorithm[J]. Computer simulation,2016,33(12):374-379.
[11] VASU V,JYOTHI KUMAR K. Optimal path planning of an autonomous mobile robot using genetic algorithm[J]. Advanced materials research,2012,488:1747-1751.