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.
@article{arxiv.2307.03355,
title = {Optimized Path Planning for USVs under Ocean Currents},
author = {Behzad Akbari and Ya-Jun Pan and Shiwei Liu and Tianye Wang},
journal= {arXiv preprint arXiv:2307.03355},
year = {2024}
}
Comments
10 pages and 8 figures, submitted for IEEE Transactions on Man, systems ,and Cybernetics