Research on Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Abstract

In this paper, we address the problem that in path planning research, the traditional ant colony algorithm suffers from slow convergence and is easily trapped in local optimal solutions. Therefore, this paper proposes an improved ant colony algorithm that can find the optimal path with fewer iterations. The pheromone initial distribution strategy based on the idea of quadrant discrimination is proposed to make the pheromone differentially distributed, thus improving the orientation of the pheromone at the early stage of the algorithm search. The heuristic function is improved by introducing the distance between the next node to the target node and the number of grids that the path passes through to increase the influence of local paths. The pheromone concentration adaptive update strategy is proposed, and the dynamic pheromone volatility factor is introduced to make the pheromone concentration vary in a controlled range. Experiments demonstrate that, under the same environment, the improved algorithm in this paper reduces the path length by 11.80% and 7.15%, and the number of convergence iterations by 55.86% and 38.82%, respectively, compared with the traditional ant colony algorithm and the algorithm in literature [6]. The improved algorithm is significantly better than the other two algorithms in terms of convergence speed and path length, which effectively shortens the optimal path length and the number of iterations of the robot, and has certain feasibility.