Abstract
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 and achieves multi-goal path planning and collision avoidance for USVs in unknown and complex environments. By expanding the adjacent search range and setting a safe distance for USVs, we solve the issue of limited steering maneuverability in USVs with fewer DOF during autonomous navigation. We propose an approach to optimize the planned path during navigation by comparing the estimated distance with the actual distance between the current waypoint and the goal waypoint. A minimum binary heap is used to optimize the priority queue of the D* Lite and significantly reduce the path search time. Simulation results show that the improved D * Lite can significantly reduce the path planning time, optimize the planned path and solve the issue of limited steering maneuverability in USVs. We apply the algorithm to a real USV for further tests. Experimental results show that the USV can plan an optimized path while avoiding both static and dynamic obstacles in complex environments with a safe distance during autonomous navigation.
Highlights
Due to the gradual deterioration in water quality [1], water quality monitoring is crucial for the protection of river systems
Path planning is a critical issue for autonomous navigation and collision avoidance in Unmanned surface vehicles (USVs)
Compared to other robots, such as UAVs, and due to the limited steering maneuverability and the fewer degrees of freedom (DOF) of USVs and the interference caused by wind and water flow, path planning is a more critical issue for autonomous navigation and collision avoidance in USVs
Summary
Due to the gradual deterioration in water quality [1], water quality monitoring is crucial for the protection of river systems. To efficiently plan a path in a scenario with a changing start node and a fixed goal node, Koenig, S et al developed a D* Lite algorithm based on the LPA* and D* algorithms [18,19]. It combines heuristic and incremental search and achieves fast path planning from a dynamic start node to a fixed goal node. This paper improves the traditional D* Lite algorithm in several ways and applies it to achieve multi-goal path planning and dynamic obstacle avoidance for USVs in unknown and complex environments. D* Lite searches reversely from the goal node to the start node for path planning This strategy is beneficial for replanning in dynamic environments. We expand the D* Lite to be a multi-goal path planning algorithm
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