Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite

Path planning and collision avoidance during autonomous navigation in unknown environments is a crucial issue for unmanned surface vehicles (USVs). This paper improves the traditional D* Lite algorithm achieves multi-goal path USVs complex environments. By expanding adjacent search range setting safe distance USVs, we solve of limited steering maneuverability with fewer DOF navigation. We propose an approach to optimize planned by comparing estimated actual between current waypoint goal waypoint. A minimum binary heap used priority queue significantly reduce time. Simulation results show that improved D * can time, USVs. apply real USV further tests. Experimental plan optimized while avoiding both static dynamic obstacles