Optimized Trajectory Planning for USVs Under Ocean Currents

The proposed work focuses on the trajectory planning for unpiloted surface vehicles (USVs) in the ocean environment, considering various spatiotemporal factors such as ocean currents and other energy consumption factors. This article uses Gaussian process motion planning (GPMP), a Bayesian optimization method that has shown promising results in continuous and nonlinear motion planning algorithms. The proposed work improves )GPMP 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.