Improved A* Algorithm Based on Artificial Potential Field Method for Global Path Planning of Unmanned Surface Vessels in Complex Environments

Aiming at the problems of long path planning time, excessive ineffective expansion nodes, and easy collision with obstacles that may occur when using traditional A* algorithm for unmanned surface vessels in complex environments, an improved A* algorithm combined with artificial potential field thinking is proposed for global path planning of unmanned surface vessels. In the improved A* algorithm, the search direction was first optimized, simplifying the traditional eight direction search to a “target point attracts four directions” search, significantly reducing the ineffective expansion nodes of the traditional algorithm, reducing algorithm running time, and improving search efficiency; Secondly, on the basis of improving the cost function, the idea of artificial potential field algorithm is integrated, and the repulsive function is introduced. Compared with the traditional A* algorithm that uses a fixed weighted cost function, it improves the algorithm’s obstacle avoidance ability and safety; Then, a redundant point deletion scheme was proposed to optimize the path nodes, effectively shortening the path length; Finally, the Bessel function was used to smooth the path. The experimental results show that in complex environments, the improved A* algorithm can reduce the average computation time by 30.5% compared to traditional A* algorithms, improve security by 52.44%, and successfully achieve the target point, verifying the effectiveness of the algorithm.