Mobile Robot Path Planning Based on Improved Smooth A* Algorithm and Optimized Dynamic Window Approach

Abstract

To address the issues of low search efficiency, redundant nodes, and poor smoothness associated with the traditional A* algorithm in robotic path planning, this paper proposes a robot path planning algorithm that integrates an improved smooth A* algorithm with the Dynamic Window Approach (DWA). A dynamic weight factor is introduced into the cost function of the A* algorithm's heuristic function to enhance path search efficiency. The global path is optimized for redundancy using the Floyd algorithm, reducing the number of turns. The optimized global path is then further processed for path circular arc smoothing, effectively eliminating rough turns and improving the overall smoothness of the path. The evaluation function of the DWA algorithm is optimized, combining the improved A* algorithm with the Dynamic Window Approach. The global path's key nodes serve as target points for the DWA algorithm, enabling segmented path planning. Simulation results demonstrate that the improved A* algorithm effectively overcomes the deficiencies of the traditional A* algorithm, and robots using the integrated algorithm can closely adhere to the globally optimal path, exhibiting improved obstacle avoidance performance in the presence of unknown obstacles.