Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles

Abstract

Operating autonomous underwater vehicles AUVs near shore is challenging-heavy shipping traffic and other hazards threaten AUV safety at the surface, and strong ocean currents impede navigation when underwater. Predictive models of ocean currents have been shown to improve navigation accuracy, but these forecasts are typically noisy, making it challenging to use them effectively. Prior work has explored the use of probabilistic planners, such as Markov decision processes MDPs, for planning in these scenarios, but prior methods have lacked a principled way of modeling the uncertainty in ocean model predictions, which limits applicability to cases in which high fidelity models are available. To overcome this limitation, we propose using Gaussian processes GPs augmented with interpolation variance to provide confidence measures on predictions. This paper describes two novel planners that incorporate these confidence measures: 1 a stationary risk-aware GPMDP for low-variability currents, and 2 a nonstationary risk-aware NS-GPMDP for faster and high-variability currents. Extensive simulations indicate that the learned confidence measures allow for safe and reliable operation with uncertain ocean current models. Field tests of the planners on Slocum gliders over several weeks in the ocean demonstrate the practical efficacy of our approach.