窄通道路径规划的改进人工势场蚁群算法
|
摘要
在全局静态环境下,提出了一种适用于窄通道环境路径规划的蚁群算法。解决了传统蚁群算法容易陷入局部最优解、易于造成蚂蚁迷失等不足。一方面,在灰度矩阵上随机撒点并将障碍物内的节点随机移动,使得窄通道内节点密度提高,并以此为启发信息素,提高了无人飞行器穿过窄通道的能力,减少了蚂蚁迷失现象。另一方面,引入了无人飞行器轨迹的尖角优化策略,更好地模拟了无人飞行器的飞行特征。结果表明:新的算法所获取的最优路径具有更好的全局搜索能力,并且造成了较少数量的蚂蚁迷失。
An improved ant colony algorithm in narrow channel environment is proposed which solves the deficiency of the traditional ant colony algorithm such as local optimum and ant"lost". On the one hand, random points evenly distributed on the plane are randomly removed from obstacles. Those points concentrate in the narrow channel which are utilized to construct heuristic information. And heuristic information is used to improve the capability to cross the narrow channel.On the other hand, sharp corners strategy is used to simulate the flight character. The results demonstrate that improved ant colony algorithm shows better search performance and lesser ant"lost".
An improved ant colony algorithm in narrow channel environment is proposed which solves the deficiency of the traditional ant colony algorithm such as local optimum and ant"lost". On the one hand, random points evenly distributed on the plane are randomly removed from obstacles. Those points concentrate in the narrow channel which are utilized to construct heuristic information. And heuristic information is used to improve the capability to cross the narrow channel.On the other hand, sharp corners strategy is used to simulate the flight character. The results demonstrate that improved ant colony algorithm shows better search performance and lesser ant"lost".
引文
[1]杨盛毅,柳阳阳,杨伟力.一种未知环境下的局部动态概率路线图法[J].航空科学技术,2016,27(4):69-73.
[2]刘洲洲.基于改进人工势场法的智能无人车路径规划仿真研究[J].计算技术与自动化,2013,32(2):133-136.
[3]刘洋,章卫国,李广文.基于改进PRM算法的路径规划研究[J].计算机应用研究,2012,29(1):104-106.
[4]赵江,张岩,马泽文,等.对AGV路径规划A星算法的改进与验证[J].计算机工程与应用,2018,54(21):217-223.
[5]李丽娜,郭永强,张晓东,等.萤火虫算法结合人工势场法的机器人路径规划[J].计算机工程与应用,2018,54(20):104-109.
[6]张捍东,陈阳,吴玉秀.未知环境下移动机器人实时路径规划[J].计算机工程与应用,2018,54(19):140-146.
[7]刘佶鑫,赵英凯.元胞贝叶斯决策路径规划方法[J].计算机工程与设计,2009,30(17):4053-4056.
[8]王娟,朱庆保,崔靖.复杂环境下基于贝叶斯决策的机器人路径规划[J].计算机工程与应用,2012,48(2):245-248.
[9]符小卫,高晓光.基于贝叶斯优化的无人机路径规划算法[J].宇航学报,2006,27(3):422-425.
[10]曾明如,徐小勇,刘亮,等.改进的势场蚁群算法的移动机器人路径规划[J].计算工程与应用,2015,51(22):33-37.
[11]朱艳,游晓明,刘升,等.基于改进蚁群算法的机器人路径规划问题研究[J].计算机工程与应用,2018,54(19):129-134.
[12]张成,凌有铸,陈孟元.改进蚁群算法求解移动机器人路径规划[J].电子测量与仪器学报,2016,30(11):1758-1764.
[13]张叶茂,杨晓武.基于改进蚁群算法的动态路径规划算法研究[J].西部交通科技,2017,12(3):86-93.
[14]赵峰,杨春曦,陈飞,等.自适应搜索半径蚁群动态路径规划算法[J].计算机工程与应用,2018,54(19):56-61.
[15]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.
[2]刘洲洲.基于改进人工势场法的智能无人车路径规划仿真研究[J].计算技术与自动化,2013,32(2):133-136.
[3]刘洋,章卫国,李广文.基于改进PRM算法的路径规划研究[J].计算机应用研究,2012,29(1):104-106.
[4]赵江,张岩,马泽文,等.对AGV路径规划A星算法的改进与验证[J].计算机工程与应用,2018,54(21):217-223.
[5]李丽娜,郭永强,张晓东,等.萤火虫算法结合人工势场法的机器人路径规划[J].计算机工程与应用,2018,54(20):104-109.
[6]张捍东,陈阳,吴玉秀.未知环境下移动机器人实时路径规划[J].计算机工程与应用,2018,54(19):140-146.
[7]刘佶鑫,赵英凯.元胞贝叶斯决策路径规划方法[J].计算机工程与设计,2009,30(17):4053-4056.
[8]王娟,朱庆保,崔靖.复杂环境下基于贝叶斯决策的机器人路径规划[J].计算机工程与应用,2012,48(2):245-248.
[9]符小卫,高晓光.基于贝叶斯优化的无人机路径规划算法[J].宇航学报,2006,27(3):422-425.
[10]曾明如,徐小勇,刘亮,等.改进的势场蚁群算法的移动机器人路径规划[J].计算工程与应用,2015,51(22):33-37.
[11]朱艳,游晓明,刘升,等.基于改进蚁群算法的机器人路径规划问题研究[J].计算机工程与应用,2018,54(19):129-134.
[12]张成,凌有铸,陈孟元.改进蚁群算法求解移动机器人路径规划[J].电子测量与仪器学报,2016,30(11):1758-1764.
[13]张叶茂,杨晓武.基于改进蚁群算法的动态路径规划算法研究[J].西部交通科技,2017,12(3):86-93.
[14]赵峰,杨春曦,陈飞,等.自适应搜索半径蚁群动态路径规划算法[J].计算机工程与应用,2018,54(19):56-61.
[15]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.