Wheeled Mobile Robot Navigation Based on the Fusion of A* Algorithm and Improved Dynamic Window Approach

The A* method is frequently utilized for strategizing navigational routes for autonomous wheeled robots, but it is not applicable to scenarios with dynamic obstacles. While the Dynamic Window Approach (DWA) algorithm can adapt to dynamic environments, it is limited in terms of path smoothing and the flexibility of adjusting the weights within its evaluation function. To address these challenges, this paper proposes a hybrid path planning strategy that integrates the A* algorithm with an enhanced DWA algorithm, aiming at improving navigation efficiency and ensuring safety of robots in environments containing both static and dynamic obstacles. Initially, we introduce the principles of the A* and DWA algorithm. Subsequently, by incorporating a smooth evaluation function into the DWA algorithm and proposing a mechanism to dynamically adjust the weights, we improve the algorithm’s local obstacle avoidance capability. Further, by combining these two algorithms, we utilize the A* algorithm for global path planning and the improved DWA algorithm for local obstacle avoidance along the path. A series of real-robot tests validate the efficiency of the suggested approach in handling static and dynamic obstacles, demonstrating its good navigation performance in complex environments.