(untitled) (Contemporary Mathematics, 2024)
Research on artificial intelligence and robotics for the ecological protection and restoration of waters has increased in importance with the promotion of national green sustainable development strategies. Autonomous Surface Vessel for Water Cleanup (ASV) research has not yet considered the uncertainty of water surface float positioning under complex current fluctuation factors. The result is that ASV has low accuracy in planning water surface float cleaning paths and its efficiency cannot meet the practical application requirements. To solve the above problems, this paper proposes a dynamic path-planning method architecture based on UAV flow velocity prediction for narrow and shallow water environments such as urban rivers and lakes. We have developed a fusion algorithm based on the progressive optimal rapid random search tree (RRT*) algorithm, DWA dynamic window method, and artificial potential field, and we have realized multi-objective water surface floating under uncertain water flow fluctuations. The location prediction of objects and optimum cleaning paths are carried out, and the method verification is carried out through numerical simulations and prototype experiments. Based on simulation and experimental results, the unmanned surface trash cleaning boat is capable of dynamically predicting trash floaters on the water surface under different water flow environments and generating an optimal cleaning path in real-time. It has certain advantages over existing planning methods in terms of accuracy and timeliness and holds significant practical significance.