Abstract: Aiming at the basic ant colony algorithm's problems of long path planning time, slow convergence speed, and high number of iterative stabilization in 2D grid maps for mobile robots, an improved adaptive elite ant colony algorithm is proposed. The algorithm improves the heuristic information function by introducing a distance parameter factor, selects the next node by using an adaptive pseudo-random state transfer rule, and also fuses the angle guidance factor into the transfer probability to reduce the search blindness, thus shortening the search time. In addition, an adaptive pheromone weight updating strategy is defined to reward the pheromone only for the optimal paths found in the contemporary search, which further improves the convergence speed. The ablation experiments, comparative experiments under different scales and environments show that the improved algorithm plans better paths and converges faster, verifying the superiority and feasibility of the algorithm.
王影, 王晓茹, 孙万龙, 刘麒. 改进自适应精英蚁群算法的机器人路径规划[J]. 吉林化工学院学报, 2024, 41(3): 1-8.
WANG Ying, WANG Xiaoru, SUN Wanlong, LIU Qi. Improved Adaptive Elite Ant Colony Algorithm for Robot Path Planning. Journal of Jilin Institute of Chemical Technology, 2024, 41(3): 1-8.
