Adaptive Path Planning of UAV Based on A* Algorithm and Artificial Potential Field Method
Jinchao Zhao, Ya Zhang, Luoyin Ning, Xuran Xiao, Chenrui Bai, Jianwu Zhang, Min Yang · Drones · 2026
This paper presents an adaptive UAV path planning algorithm, A*-APF, which combines the A* algorithm with the artificial potential field method (APF) to overcome challenges such as lengthy paths, lack of smoothness, and local optima in traditional path planning algorithms within intricate environments. The A*-APF algorithm utilizes the global heuristic search abilities of A* and integrates a dynamic adaptive mechanism for gravitational and repulsive coefficients based on target distance, obstacle density, and path curvature. This mechanism enables real-time adjustments of potential field parameters, improving both global optimality and local path smoothness. Simulation results demonstrate that the A*-APF algorithm surpasses A*, RRT, PRM, and GWO algorithms in terms of path length, smoothness, computational efficiency, and stability. Specifically, it reduces the average path length by 15–25%, enhances smoothness by 30–45%, and decreases computation time by nearly 90%. Physical experiments confirm that the algorithm achieves the shortest path, optimal obstacle avoidance, and superior stability in real-world environments, highlighting its global optimization capability, real-time performance, and potential for engineering applications in complex dynamic environments. These results emphasize the algorithm’s ability to enhance UAV stability during task execution.