Path Planning Based on Improved Hybrid A^* Algorithm

Abstract

Hybrid A^* algorithm has been widely used in mobile robots to obtain paths that are collision-free and drivable. However, the outputs of hybrid A^* algorithm always contain unnecessary steering actions and are close to the obstacles. In this paper, the artificial potential field (APF) concept is applied to optimize the paths generated by the hybrid A^* algorithm. The generated path not only satisfies the non-holonomic constraints of the vehicle, but also is smooth and keeps a comfortable distance to the obstacle at the same time. Through the robot operating system (ROS) platform, the path planning experiments are carried out based on the hybrid A^* algorithm and the improved hybrid A^* algorithm, respectively. In the experiments, the results show that the improved hybrid A^* algorithm greatly reduces the number of steering actions and the maximum curvature of the paths in many different common scenarios. The paths generated by the improved algorithm nearly do not have unnecessary steering or sharp turning before the obstacles, which are safer and smoother than the paths generated by the hybrid A^* algorithm for the autonomous ground vehicle.