A hybrid optimization algorithm combining A* and the dynamic window approach for automated guided vehicle control in static and dynamic environments

Abstract This study presents the control of an omnidirectional automated guided vehicle (AGV) with mecanum wheels using a hybrid optimization algorithm that combines a modified A* algorithm and the dynamic window approach (ADWA-HO). The method ensures efficient and precise navigation in both static and dynamic environments. The modified A* algorithm generates global paths, removes redundant nodes, and refines trajectories to improve efficiency and smoothness. At the same time, the dynamic window approach (DWA) enables real-time local path planning and obstacle avoidance. By evaluating the AGV’s motion commands in real time, ADWA-HO selects optimal velocity commands within a dynamically updated window, thereby reducing route conflicts and ensuring stable movement. Compared with benchmark methods including dynamic A* (D*), artificial potential field (APF), DWA, probabilistic roadmap (PRM) & rapidly exploring random tree (RRT) fusion, and PRM & DWA fusion, the proposed ADWA-HO achieves improvements in average path length of 28.10%, 22.95%, 21.16%, 17.35%, and 10.71% and in average motion time of 23.48%, 17.85%, 15.47%, 11.86%, and 7.53% on both Map 1 and Map 2, respectively. The difference between simulation and real-world experiments is limited to 5.35% in path length and 4.38% in motion time, confirming the method’s practical reliability. Furthermore, the algorithm achieves lower standard deviation in both metrics, indicating higher consistency of performance. This work also introduces a novel map-building strategy based on geometric and semantic data modules, which enhances the adaptability of real-world AGV deployment.