Dynamic obstacle avoidance path planning method for autonomous driving based on quantum ant colony algorithm

Addressing the challenges of extensive obstacle avoidance distances and sluggish response times inherent in conventional obstacle avoidance path planning approaches, a dynamic obstacle avoidance path planning method for autonomous driving based on quantum ant colony algorithm is proposed. Firstly, the free space method is used to construct the environmental model. Then, in the environmental model, the ant colony algorithm is used to calculate the heuristic function between nodes and update pheromones, thus generating the automatic driving path. Finally, the quantum ant colony algorithm is used to select the dynamic obstacle avoidance path among the generated automatic driving paths, so as to realize the dynamic obstacle avoidance path planning of automatic driving. The experiments show that the shortest obstacle avoidance distance of this method is 0.915m and 1.292m shorter than that of the two experimental comparison methods respectively, and the obstacle avoidance response time is always less than 1.0s.