Abstract: In order to address the drawbacks of traditional ant colony algorithm in mobile robot path planning,such as blind search,slow convergence speed,multiple path turning points,it proposes a mobile robot path planning algorithm based on improved ant colony algorithm.Firstly,the Jump Point Search (JPS) algorithm is utilized to unevenly distribute initial pheromones,reducing the likelihood of blind search during the early stages of the ant colony.Then,a Chebyshev distance weighting factor and turning cost are introduced to improve the heuristic function,enhancing the algorithm′s convergence speed,global path optimization capability,and smoothness of the search path.Finally,a novel pheromone update strategy is proposed that introduces an adaptive reward-punishment factor to adaptively adjust the pheromone reward-punishment factor during pre-and post-iteration phases,ensuring the algorithm′s global optimal convergence.Experimental simulation results demonstrate that,in various map environments and compared to existing literature results,the proposed algorithm effectively reduces the number of iterations and optimal path length required for path search while increasing path smoothness.

Key words: ant colony algorithm, path planning, Jump Point Search (JPS) algorithm, mobile robot, pheromone heuristic