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Aiming at the problems of poor convergence and local optimization in path planning using the basic ant colony algorithm, this paper studies an improved ant colony algorithm to enhance the effect of mobile robot path planning. Firstly, the state transition probability of the ant colony algorithm is modified, and the influence of the angle on node selection is increased by adding a new angle index heuristic function. Then, the sorting and elite ant colony algorithm strategies are fused to research a path length difference pheromone update method, improving the efficiency of the ant colony algorithm in planning the optimal path. Finally, through a comparison with the basic ant colony algorithm simulation on MATLAB, the feasibility and effectiveness of the improved ant colony algorithm are verified.
Wu et al. (Wed,) studied this question.