关键词: 蚁群算法, 路径规划, 局部分块优化策略, 增强因子, 反向学习

Abstract:

Aiming at the problems of traditional ant colony algorithm in mobile robot path planning, such as easy to fall into local optimum and slow convergence speed, an improved ant colony algorithm is proposed. Firstly, the global optimization region is constructed according to the distance from the starting point to the end point and the map parameters, so as to improve the initial pheromone concentration in the region, and avoid blind search at the beginning of the algorithm. Secondly, the local block optimization strategy is used to optimize each sub-region and update the optimal path information in the region, so as to enhance the local search ability and speed up the convergence speed. Finally, the global path is optimized and the global optimal path pheromone is updated. The pheromone enhancement factor is introduced into the pheromone updating formula to enhance the pheromone content of the optimal path. The opposition-based learning is applied to optimize pheromone, and improve the probability of state selection, so as to improve the optimization ability of the algorithm. The experimental results show that the improved algorithm significantly improves the convergence speed and has stronger optimization ability.

Key words: ant colony optimization, path planning, local block optimization strategy, enhancement fact, opposition-based learning