LAUSR

A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.