Path Planning for Mobile Robots with an A-Star and Artificial Potential Field Fusion Algorithm
Yanqing Liu, Hongjun Xing, Jinbao Chen, Yuzhe Xu, Y. G. Xie, Chen Wang · 2024
With the rapid advancement of artificial intelligence, path planning has emerged as a crucial technology in the deployment of mobile robots. In order to optimize the path of a mobile robot, aiming at the problem that the traditional A* algorithm has many turns in path planning and the artificial potential field method is easy to fall into the local optimum, this paper proposes a fusion algorithm combining the A* algorithm and the artificial potential field method. The algorithm aims to avoid static obstacles on the ground such as desks and tables, and reach the target mission point safely and efficiently. A* algorithm is used to generate the optimal safe path from the starting point to the end point in the raster map. According to the optimal path generated by A* algorithm, a global path segmentation method is designed, and local path planning based on the artificial potential field method is carried out. Compared with the conventional A* algorithm, the proposed fusion algorithm generates a shorter motion path with fewer turning times, and the artificial potential field algorithm is added for local obstacle avoidance, which greatly improves the safety of the wheeled mobile robot's movement. Finally, simulations have been conducted to prove the effectiveness of the proposed method.