Abstract:

An improved ant colony algorithm for robot path planning is proposed based on the defects of the ant colony algorithm, such as slow convergence speed, easy to get into local optimization, and weak optimization ability in complex environment. An initial favorable pheromone matrix is established on the basis of pre-planned paths to effectively avoid blind search in the early stage of the algorithm to improve the directionality. The improved colony algorithm is fused with A* algorithm to further improve the directionality and convergence speed of algorithm search. The pheromone update rule introduces a turning evaluation function to improve the smoothness of the search path, improve the safety of robots and reduce energy consumption. A rollback strategy is proposed to effectively reduce the number of dead ants and improve the robustness of the algorithm search. Simulation experiments show that in the same environment, the improved ant colony algorithm performs better than other algorithms in search efficiency and convergence speed during the process of robot path planning.

Key words: ant colony algorithm, path planning, heuristic function, inflection point evaluation function

摘要：

基于蚁群算法在路径规划过程中出现收敛速度慢、易陷入局部最优，且在复杂环境下的寻优能力弱等缺陷，提出了一种适用于机器人路径规划的改进蚁群算法。在预规划路径基础上建立初始信息素矩阵，避免算法前期盲目搜索，提高搜索速度；将改进蚁群算法和A*算法进行有机融合，进一步提高蚁群算法搜索方向性和收敛速度。制定信息素更新规则时引入拐点评价函数，提高搜索路径的光滑性，提高机器人安全性和降低能耗；提出回退策略有效减少蚂蚁死亡数量，提高路径规划方法的鲁棒性。仿真实验表明，在相同的环境下，改进的蚁群算法在机器人路径规划中搜索效率和收敛速度明显优于其他算法。

关键词: 蚁群算法, 路径规划, 启发函数, 拐点评价函数