A Hybrid Path Planning Method Based on Improved A* and APF Algorithms
Chuan Liu, Bikang Hua, Senchun Chai, Runqi Chai, Lingguo Cui · 2025
In order to address the deficiencies in the$\mathbf{A}^{*}$algorithm and the Artificial Potential Field algorithm, this paper proposed an enhanced hybrid path planning approach based on improvements to the$A^{*}$algorithm and the Artificial Potential Field algorithm. Firstly, we dilate static obstacles in A* map to make the$A^{*}$trajectory more realistic. Also, to address the issues of redundant path nodes and non-smooth paths generated by the A* algorithm, we introduce enhancements such as the removal of redundant path nodes and the incorporation of a third-order quasi-uniform B-spline curve to get shorter and smoother paths. Secondly, to tackle the issue of unreachable targets, we modify the attraction function. Additionally, to address the problem of insufficiently smooth trajectories during obstacle avoidance with the APF algorithm, we constrain the vehicle's steering angle to generate a smoother path. Finally, for dangerous moving obstacles, we predict their motion trajectories using B-spline curves and then perform obstacle avoidance. By combining the improved A* algorithm and APF algorithm, we provide a better solution for path planning in mobile robots.