关键词: 蚁群算法, 启发函数, 路径规划, 移动机器人, 信息素

Abstract: The path planning of mobile robots not only requires short path distances, but also avoids excessive turning of paths, serious bumps, and poor environmental adaptability. Therefore, this paper proposes improvement heuristics function based on three factors：path length, number of turns, and smoothness of gradient, comprehensively calculating of transition probability. While improving the pheromone update method, it allocates the pheromone amount on each path according to the three-factor comprehensive index, guides ants to approach the path with the best overall performance. And it proposes a non-uniform initial pheromone method to prevent excessive ants into the dead end. It combines improved map modeling barriers to improve path safety. Simulation and experimental results show that the planning path obtained by the improved algorithm has a great improvement in the overall performance of the three factors, and has a good global search capability and convergence. Adjusting the parameters appropriately can also obtain a path with a prominent characteristic. Both the number of iterations and the calculation time perform better.

Key words: Ant Colony Algorithm（ACA）, inspired function, grid path planning, mobile robot, pheromone