Application of improved ant colony algorithm in mobile robot path planning

Abstract

Aiming at the shortcomings of slow convergence speed and many turning points of the traditional ant colony algorithm in the global path planning of mobile robot, an improved ant colony algorithm is proposed. By adding obstacle information to improve the distance heuristic function, the probability of ants entering the deadlock path is reduced, and the number of effective search is increased. The adaptive adjustment strategy is used to design the dynamic adjustment mechanism of pheromone heuristic factor, which improves the development of the algorithm in the initial stage and takes into account the convergence in the later stage. The second search strategy is introduced to simplify the path points of ant colony algorithm and reduce the redundancy. Experiments show that the improved algorithm has good adaptability in complex environments, and the comprehensive performance index of the final path is better than that of the traditional algorithm, which can provide effective reference for the global path planning of robots in practical environments.