A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

Abstract

To address the problems of the traditional A ∗ algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A ∗ algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A ∗ algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A ∗ and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.