A 3D-Sparse A* autonomous recovery path planning algorithm for Unmanned Surface Vehicle

Abstract

Thanks to the development of marine intelligent technology and equipment, the Unmanned Surface Vehicle (USV) has been brought into focus for its exceptional application. To overcome the challenge of the stern ramp recovery for USVs, an advanced 3D-Sparse A* path planning algorithm has been proposed. This algorithm incorporated sparse sampling and the time dimension to optimize the generation of waypoints, resulting in a smaller computation load. Dynamic constraints and evaluation functions were integrated to reconstruct the algorithm’s framework. Simulations suggest that planning speed can be increased by a factor of 20, improving recovery efficiency. The path’s smoothness was elevated, with a maximum curvature 50% lower than the original version. Furthermore, the malposition was reduced to 54.78% of the original, meeting the requirements for particular attitude adjustment during stern ramp recovery. Field experiment indicates that the actual results have a maximum velocity deviation of 0.61 m/s and a maximum course deviation of 1.61 m compared to the planning results. The 3D-Sparse A* algorithm can complete the recovery within a distance of 100 m in 60 s, assuming an unobstructed trajectory. This verifies the feasibility and planning efficiency of the algorithm.