An improved ant colony algorithm for robot path planning
An improved ant colony algorithm for robot path planning
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DOI：
10.1007/s00500-016-2161-7
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发表时间：
2017-10-01
期刊：
影响因子：
4.1
通讯作者：
Gao, Meng
中科院分区：
文献类型：
--
作者：
Liu, Jianhua;Yang, Jianguo;Gao, Meng
针对蚁群算法收敛速度慢的问题，提出了一种改进的蚁群优化算法，用于移动的机器人在栅格法表示的环境中的路径规划。在搜索全局最优路径的过程中结合信息素扩散和几何局部优化，蚂蚁搜索过程中当前路径信息素向势场力方向扩散，使蚂蚁倾向于搜索适应度更高的子空间，测试模式的搜索空间变小。首先使用蚁群算法优化的路径使用几何算法优化。同时更新第一最优路径和第二最优路径的信息素。仿真结果表明，改进的蚁群优化算法是有效的.
To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.