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.

中文翻译：

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基于人工矢量场和环境启发式的无人水面车辆低成本路径在线规划

该研究关注在线规划低成本合作路径的问题；这些是基于人工矢量场和环境启发式的无人水面车辆 (USV) 的节能、易于执行和低碰撞概率。首先，我们提出了一种人工矢量场方法，通过遵循全局最优路径和电流来最大化已知环境信息。然后，为了通过环境启发式改进基于最优快速探索随机树（RRT*）的规划器，采用高斯采样方案来寻找位于障碍物附近的可能样本。同时，提出了一种多重采样策略，以在本地选择低成本的路径树扩展。矢量场引导，高斯采样方案，并且采用多重采样策略提高RRT*的效率，为USV的虚拟领导者获取低成本路径。为提高RRT*执行过程中碰撞检测的准确性，结合当前情况，提出了一种基于椭圆函数的USV边界框。最后，采用信息共识方案来快速计算由虚拟领导者引导的一组 USV 的合作路径。仿真结果表明，我们的在线协同路径规划方法在实际海洋环境中表现良好。采用信息共识方案来快速计算由虚拟领导者引导的一组 USV 的合作路径。仿真结果表明，我们的在线协同路径规划方法在实际海洋环境中表现良好。采用信息共识方案来快速计算由虚拟领导者引导的一组 USV 的合作路径。仿真结果表明，我们的在线协同路径规划方法在实际海洋环境中表现良好。