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The proposed work focuses on the path planning for Unmanned Surface Vehicles(USVs) in the ocean enviroment, taking into account various spatiotemporalfactors such as ocean currents and other energy consumption factors. The paperproposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesianoptimization method that has shown promising results in continuous andnonlinear path planning algorithms. The proposed work improves GPMP2 byincorporating a new spatiotemporal factor for tracking and predicting oceancurrents using a spatiotemporal Bayesian inference. The algorithm is applied tothe USV path planning and is shown to optimize for smoothness, obstacleavoidance, and ocean currents in a challenging environment. The work isrelevant for practical applications in ocean scenarios where an optimal pathplanning for USVs is essential for minimizing costs and optimizing performance.

Optimized Path Planning for USVs under Ocean Currents