改进蚁群算法在AGV路径规划上的研究

Research on improved ant colony algorithm in AGV path planning

针对蚁群算法应用于路径规划过程中存在算法收敛速度慢、易陷入局部最优等缺陷,提出一种适用于AGV路径规划的改进蚁群算法。根据距离在栅格地图上差异化分布初始信息素浓度,避免蚁群前期进行盲目搜索,加快算法收敛速度;综合当前栅格与待选栅格之间的距离以及待选栅格与目标栅格之间的距离改进启发式函数,增加蚁群寻路方向性;引入动态启发因子,避免算法发生“早熟”现象以及陷入局部最优;对每个栅格的邻近栅格进行方向标号,增加最优路径与障碍物之间的距离,增强最优路径的安全性,同时避免“死角”现象发生,提升算法的鲁棒性。实验仿真结果表明,在相同环境下,改进算法在AGV路径规划中搜索效率和迭代稳定性方面均优于其它算法。

In view of the shortcomings of ant colony algorithm in the process of path planning,such as low convergence speed and easiness to fall into local optimization,an improved ant colony algorithm suitable for AGV path planning was proposed.The initial pheromone concentration was differentiated on the grid map according to the distance,which avoided the blind search in the early stage of the ant colony and speeded up the convergence speed of the algorithm.The distance between the current grid and the grid to be selected and the distance between the grid to be selected and the target grid were synthesized to improve the heuristic function to increase the direction of ant colony pathfinding.Dynamic heuristic factor was introduced to avoid the phenomenon of prematurity and falling into local optimization.It was proposed to label the direction of the adjacent grid of each grid,which increased the distance between the optimal path and obstacles,enhanced the security of the optimal path,avoided the occurrence of dead corner phenomenon,and improved the robustness of the algorithm.The simulation results show that in the same environment,the search efficiency and iterative stability of the improved algorithm are better than that of other algorithms in AGV path planning.