An Adaptive Ant Colony Algorithm for Autonomous Vehicles Global Path Planning

Abstract

In order to improve the robustness of the autonomous vehicle path planning algorithm and reduce the number of turns in the planned path, this paper proposes an adaptive ant colony algorithm path planning method. The algorithm optimizes the initial pheromone matrix based on the environment map, reduces the blindness of the initial ant colony in pathfinding, and improves the convergence speed. Then an adaptive heuristic function is used, which adaptively adjusts according to the different proportions of the heuristic function in the algorithm process, so as to avoid the algorithm being trapped in local optimum. The pheromone is updated according to the corners of the planned route, reducing the acute angle of the route and unnecessary turns to further optimize the route. The simulation results show that the proposed algorithm achieves good results. The simulation results show that the improved adaptive ant colony algorithm has faster convergence speed, higher path planning quality, and improved stability of planned paths than classical ant colony algorithms and other adaptive ant colony algorithms.