PATH PLANNING FUSION ALGORITHM BASED ON IMPROVED A-STAR AND ADAPTIVE DYNAMIC WINDOW APPROACH FOR MOBILE ROBOT - Zhao, Qiwei; Liu, Hao; Zhang, Yiying; Wang, Jingkai (International Journal of Industrial Engineering: Theory, Applications and Practice, 2023)
To achieve the path planning of mobile robots in a complex, unknown environment, the conventional A-star (A∗) algorithm has been enhanced for global path optimization, and the dynamic window approach (DWA) is incorporated for dynamic, real-time obstacle avoidance. Addressing issues of inadequate real-time performance and adaptability of the path planning method, we propose a fusion path planning method for mobile robots based on real-time positioning and map construction by vision sensors, which includes a local potential field A* algorithm (LPF-A*) and an adaptive dynamic window approach (ADWA). Initially, the proportion of obstacles in the grid environment is assessed, and this ratio is incorporated into the traditional A* algorithm to optimize the heuristic function, augment the evaluation function and boost its search efficiency in varying environments. Subsequently, in view of the intersection issue between the path optimized by the conventional A* algorithm and obstacle vertices in a complex grid environment, the search neighborhood of the A* algorithm is expanded to decrease the number of child nodes for path planning, remove superfluous nodes in the path planning, and enhance path smoothness. Ultimately, the ADWA is integrated to facilitate dynamic real-time obstacle avoidance for mobile robots in a complex setting. Compared to the traditional A* algorithm and the conventional DWA, the proposed fusion path planning algorithm improves track length, track smoothness, and search efficiency, meeting the needs for a globally optimal path and enabling dynamic real-time obstacle avoidance.