Robot Path Planning Based on Improved A* Algorithm and Artificial Potential Field Method

Abstract

In response to the problems of long planning paths, large turning angles, and inability to avoid dynamic obstacles in traditional A* algorithm robot path planning, this paper proposes a path planning algorithm that combines improved A* algorithm with artificial potential field method. Firstly, the improved A* algorithm searches the neighborhood and heuristic function. Compared with some algorithms, the improved A* algorithm reduces the optimal path distance, search nodes, simulation time, and turning angles by 22.78%, 80.65%, 69.84%, and 50% respectively. The improved A* algorithm is further optimized by removing redundant nodes and smoothing the path, reducing the optimal path, simulation time, and turning angles by 2.08%, 9.1%, and 36.36% respectively compared to the first optimization. For local path planning using artificial potential field, the artificial potential field function and adaptive step size are improved. Simulation results show that the improved algorithm can overcome the problems of local minima and unreachable targets. Finally, the integrated algorithm simulation shows that it can solve the problem of A* algorithm s inability to avoid dynamic obstacles and guide the robot to move along the optimal path.