基于改进蚁群算法的机器人路径规划问题研究
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摘要
针对基本蚁群算法前期搜索效率低下以及在寻优的过程中会穿过障碍物等问题,提出改进的蚁群算法,即采用动态调整启发因子、信息素初始化改进策略、可选节点的筛选机制方案进行改进工作。通过对基本蚁群算法和改进蚁群算法的仿真结果分析可知,改进后算法的最优路径长度虽然有所增加,但减少了蚂蚁前期到达最优路径的迭代次数,得到一条无碰撞、没有穿过障碍物的路径,且耗时与基本蚁群算法相持平,保证了机器人路径的安全性,提高了算法的前期搜索效率。
Aiming at the low efficiency of the basic ant colony algorithm in the early stage and the obstacles in the process of optimization, an improved ant colony algorithm was proposed, which adopts dynamic adjustment heuristic factor, pheromone initialization improvement strategy and optional node screening mechanism. By analyzing the simulation results of the basic ant colony algorithm and the improved ant colony algorithm, the optimal path length of the improved algorithm is increased, but the number of iterations of the ant reaching the optimal path is reduced. A path without collision and obstacles is obtained, and the time consumption is equal to that of the basic ant colony algorithm, which ensures the safety of the robot path and improves the early search efficiency of the algorithm.
Aiming at the low efficiency of the basic ant colony algorithm in the early stage and the obstacles in the process of optimization, an improved ant colony algorithm was proposed, which adopts dynamic adjustment heuristic factor, pheromone initialization improvement strategy and optional node screening mechanism. By analyzing the simulation results of the basic ant colony algorithm and the improved ant colony algorithm, the optimal path length of the improved algorithm is increased, but the number of iterations of the ant reaching the optimal path is reduced. A path without collision and obstacles is obtained, and the time consumption is equal to that of the basic ant colony algorithm, which ensures the safety of the robot path and improves the early search efficiency of the algorithm.
引文
[1] 刘琨.基于人工势场和蚁群算法的无人船路径规划研究[D].海口:海南大学,2016.
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[8] 朱磊,樊继壮,赵杰,等.基于栅格法的矿难搜索机器人全局路径规划与局部避障[J].中南大学学报(自然科学版),2011,42(11):3421-3428.
[9] Erin B,Abiyev R,Ibrahim D.Teaching robot navigation in the presence of obstacles using a computer simulation program[J].Procedia-Social and Behavioral Sciences,2010,2(2):565-571.
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[11] 高颖,陈旭,周士军,等.基于改进蚁群算法的多批次协同三维航迹规划[J].西北工业大学学报,2016,34(1):41-46.
[12] 柴寅,唐秋华,邓明星,等.机器人路径规划的栅格模型构建与蚁群算法求解[J].机械设计与制造,2016(4):178-181.
[13] 欧阳鑫玉,杨曙光.基于势场栅格法的移动机器人避障路径规划[J].控制工程,2014,21(1):134-137.
[14] 张琦,马家辰,谢玮,等.基于改进蚁群算法的移动机器人路径规划[J].东北大学学报(自然科学版),2013,34(11):1521-1524.
[15] 柳长安,鄢小虎,刘春阳,等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报,2011,39(5):1220-1224.
[2] 康冰,王曦辉,刘富.基于改进蚁群算法的搜索机器人路径规划[J].吉林大学学报(工学版),2014,44(4):1062-1068.
[3] 罗荣贵,屠大维.栅格法视觉传感集成及机器人实时避障[J].计算机工程与应用,2011,47(24):233-235.
[4] 石铁峰.改进遗传算法在移动机器人路径规划中的应用[J].计算机仿真,2011,28(4):193-195,303.
[5] 周明秀,程科,汪正霞.动态路径规划中的改进蚁群算法[J].计算机科学,2013,40(1):314-316.
[6] 王爱民,史庆国,吕晨亮.机器人路径规划的神经网络算法及优化[J].河北工业大学学报,1999,28(6):13-16.
[7] 潘杰,王雪松,程玉虎.基于改进蚁群算法的移动机器人路径规划[J].中国矿业大学学报,2012,41(1):108-113.
[8] 朱磊,樊继壮,赵杰,等.基于栅格法的矿难搜索机器人全局路径规划与局部避障[J].中南大学学报(自然科学版),2011,42(11):3421-3428.
[9] Erin B,Abiyev R,Ibrahim D.Teaching robot navigation in the presence of obstacles using a computer simulation program[J].Procedia-Social and Behavioral Sciences,2010,2(2):565-571.
[10] Mahdi H,Reza Z F,Hannaneh R B.An ant colony-based algorithm for finding the shortest bidirectional path for automated guided vehicles in a block layout[J].The International Journal of Advanced Manufacturing Technology,2013,64(1):399-409.
[11] 高颖,陈旭,周士军,等.基于改进蚁群算法的多批次协同三维航迹规划[J].西北工业大学学报,2016,34(1):41-46.
[12] 柴寅,唐秋华,邓明星,等.机器人路径规划的栅格模型构建与蚁群算法求解[J].机械设计与制造,2016(4):178-181.
[13] 欧阳鑫玉,杨曙光.基于势场栅格法的移动机器人避障路径规划[J].控制工程,2014,21(1):134-137.
[14] 张琦,马家辰,谢玮,等.基于改进蚁群算法的移动机器人路径规划[J].东北大学学报(自然科学版),2013,34(11):1521-1524.
[15] 柳长安,鄢小虎,刘春阳,等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报,2011,39(5):1220-1224.