基于改进蚁群算法的移动机器人路径规划

Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

针对传统蚁群算法在移动机器人最短路径规划方面存在的不足,如算法前期盲目性搜索、收敛速度慢、消耗时间长及转弯次数多,提出了一种改进的蚁群算法。该算法根据正态分布模型,将栅格环境划分不同区域,进行信息素差异化处理,减少蚂蚁初期搜索时间;同时基于A*搜索算法的估价函数思想改进启发函数,引入自适应启发信息因子,增强其目标导向性,提高算法收敛速度,平衡算法全局搜索能力。仿真结果表明,改进的蚁群算法能够规划出收敛速度较快、转弯次数较少以及平滑度更高的路径。

Aiming at the shortcomings of traditional ant colony algorithm in the shortest path planning of mobile robots,such as blind search in the early stage of the algorithm,slow convergence speed,long consumption time and many turns,an improved adaptive ant colony algorithm is proposed.According to the normal distribution model,the algorithm divides the grid environment into different regions and performs pheromone differentiation processing to reduce the initial search time of ants.At the same time,the heuristic function is improved based on the evaluation function idea of A*search algorithm,and the adaptive heuristic information factor is introduced to enhance its goal orientation,improve the convergence speed of the algorithm,and balance the global search ability of the algorithm.The simulation results show that the improved ant colony algorithm can plan the path with faster convergence speed,fewer turns and higher smoothness.