Abstract: To enhance the autonomous navigation and obstacle avoidance capabilities of Automated Guided Vehicles(AGV),this study addresses the issues of poor path smoothness,non-optimal path length,and collision susceptibility inherent in the A^* algorithm.We propose an AGV path planning method that integrates an improved A^* algorithm with the Timed Elastic Band(TEB) algorithm.Initially,the search domain is expanded to 12 directions based on certain rules,broadening the AGV’s search horizon and making the search more directional.Nextly,by incorporating an obstacle factor into the heuristic function,the function can adaptively change according to the distribution of obstacles on the map,effectively reducing estimation errors.Finally,the globally optimal path planned by the improved A^* algorithm is decomposed into global waypoints.Between these waypoints,the TEB algorithm is used for local path planning,ensuring that the AGV can dynamically avoid obstacles in real-time while following the globally optimal path.Simulations demonstrate that the improved A^* algorithm significantly reduces the number of turns,path length,and nodes.An AGV experimental platform with an omnidirectional Mecanum wheel chassis was then constructed to test the integrated algorithm’s performance in autonomous navigation and obstacle avoidance.The results show that the proposed algorithm can effectively reduce the path length and travel time of AGV,ensuring safe arrival at the target point,thereby validating its superiority.

Key words: AGV, Path planning, A^* algorithm, TEB algorithm, Fusion algorithm