Online planning low-cost paths for unmanned surface vehicles based on the artificial vector field and environmental heuristics

The study is concerned with the problem of online planning low-cost cooperative paths; those are energy-efficient, easy-to-execute, and low collision probability for unmanned surface vehicles (USVs) based on the artificial vector field and environmental heuristics. First, we propose an artificial vector field method by following the global optimally path and the current to maximize the known environmental information. Then, to improve the optimal rapidly exploring random tree (RRT*) based planner by the environment heuristics, a Gaussian sampling scheme is adopted to seek for the likely samples that locate near obstacles. Meanwhile, a multisampling strategy is proposed to choose low-cost path tree extensions locally. The vector field guidance, the Gaussian sampling scheme, and the multisampling strategy are used to improve the efficiency of RRT* to obtain a low-cost path for the virtual leader of USVs. To promote the accuracy of collision detection during the execution process of RRT*, an ellipse function-based bounding box for USVs is proposed with the consideration of the current. Finally, an information consensus scheme is employed to quickly calculate cooperative paths for a fleet of USVs guided by the virtual leader. Simulation results show that our online cooperative path planning method is performed well in the practical marine environment.