  Unmanned Surface Vehicles (USVs) in the ocean environment, considering
various spatiotemporal factors such as ocean currents and other energy
consumption factors. The paper uses Gaussian Process Motion Planning (GPMP2), a
Bayesian optimization method that has shown promising results in continuous and
nonlinear motion planning algorithms. The proposed work improves GPMP2 by
incorporating a new spatiotemporal factor for tracking and predicting ocean
currents using a spatiotemporal Bayesian inference. The algorithm is applied to
the USV path planning and is shown to optimize for smoothness, obstacle
avoidance, and ocean currents in a challenging environment. The work is
relevant for practical applications in ocean scenarios where optimal path
planning for USVs is essential for minimizing costs and optimizing performance.
