Mobile Robot Path Planning Based on Improved Ant Colony Algorithm

Abstract

This article proposes an improved ant colony algorithm to solve the problems of blindness, long search path, slow convergence speed, and excessive number of turns in the initial stage of ordinary ant colony algorithms in mobile robot path planning. The algorithm is based on a normal distribution model, dividing the grid environment into different regions, and processing pheromones separately to reduce the initial search time of ants; At the same time, drawing on the evaluation function of the A 'search algorithm, the heuristic function is improved by introducing an adaptive heuristic information factor to enhance its goal orientation, improve the convergence speed of the algorithm, and balance the global search ability of the algorithm. The simulation results show that the improved ant colony algorithm proposed in this paper can plan better paths with faster convergence speed, fewer turns, and higher smoothness.