融合蚁群-A *算法的移动机器人路径规划

Mobile robot path planning based on a fusion ant colony-A*algorithm

针对蚁群算法进行路径规划时,收敛速度慢,容易陷入“自锁”,且不易寻找到最优路径等问题,提出了一种融合蚁群-A*算法来进行求解。引入A*算法的估价函数,对蚁群算法的启发函数和信息素更新方式进行改进调整,降低其陷入“自锁”的可能性,从而能够快速寻找到最优的路径。最后用Matlab进行仿真实验,实验结果表明:算法在收敛速度上提高了近40%,并且在环境模型1和2中的最优路径分别为35.6706 m和29.7990 m,优于蚁群算法的37.7990 m和32.2132 m。

When the path planning is carried out for the traditional ant colony algorithm,the convergence speed is slow and it is easy to fall into the“self-locking”,and it is not easy to find the optimal path.A fusion ant colony-A*algorithm is proposed to solve the problem.The evaluation function of A*algorithm is introduced to improve and adjust the heuristic function and pheromone update mode of traditional ant colony algorithm,and reduce the possibility of“self-locking”,so that the optimal path can be quickly found.Finally,experimenTSare carried out on Matlab simulation software.The experimental resulTSshow that the proposed algorithm improves the convergence speed by nearly 40%,and the optimal paths in environmental models 1 and 2 are 35.6706 m and 29.7990 m better than the 39.7990 m and 32.2132 m of the ant colony algorithm.