Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles

Abstract

We discuss the problem of learning uncertainty models of ocean processes to assist in the operation of Autonomous Underwater Vehicles (AUVs) in the ocean. We focus on the prediction of ocean currents, which have significant effect on the navigation of AUVs. Available models provide accurate prediction of ocean currents, but they typically do not provide confidence estimates of these predictions. We propose augmenting existing prediction methods with variance measures based on Gaussian Process (GP) regression. We show that commonly used measures of variance in GPs do not accurately reflect errors in ocean current prediction, and we propose an alternative uncertainty measure based on interpolation variance. We integrate these measures of uncertainty into a probabilistic planner running on an AUV during a field deployment in the Southern California Bight. Our experiments demonstrate that the proposed uncertainty measures improve the safety and reliability of AUVs operating in the coastal ocean.