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

Abstract

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.

Recent advances in Rapidly-exploring random tree: A review

“…The strategy maximizes information collection while working within a limited budget. Other applications include circular sampling strategy for autonomous vehicles [ 133 ], path planning for a UAV in communication-constrained operating environments [ 134 ], flight cost-based RRT for energy-efficient industrial robot motion planning [ 24 ], vector field stream based RRT* to plan unmanned surface vehicle paths under spatially variable ocean currents [ 135 , 136 ].…”

Collision Avoidance Strategy for Unmanned Surface Vessel Considering Actuator Faults Using Kinodynamic Rapidly Exploring Random Tree-Smart and Radial Basis Function Neural Network-Based Model Predictive Control

“…When it comes to the USVs' path planning, Mao et al [15] proposed a state prediction RRT for USVs by taking into account complete dynamic constraints. In [16], a novel planning algorithm based on the artificial vector field method and RRT* was proposed for USV low-cost path planning. Zhang et al [17] improved the feasibility and efficiency of the planned path by utilizing the dual sampling space strategy and the Dubins curve.…”