Optimized Path Planning for USVs under Ocean Currents

TL;DR

This paper enhances Gaussian Process Motion Planning for USVs by integrating spatiotemporal ocean current predictions, resulting in improved path optimization considering smoothness, obstacle avoidance, and energy efficiency.

Contribution

It introduces a novel spatiotemporal Bayesian inference method to improve GPMP2 for USV path planning in dynamic ocean environments.

Findings

Optimized USV paths considering ocean currents and obstacles.

Improved path smoothness and energy efficiency.

Effective prediction of ocean currents enhances planning accuracy.

Abstract

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.