Development and Online Validation of an UKF-based Navigation Algorithm for AUVs

The development of precise and robust navigation strategies for Autonomous Underwater Vehicles (AUVs) is fundamental to reach the high level of performance required by complex underwater tasks, often including more than one AUV. One of the main factors affecting the accuracy of AUVs navigation systems is the algorithm used to estimate the vehicle motion, usually based on kinematic vehicle models and linear estimators. In this paper, the authors present a navigation strategy specifically designed for AUVs, based on the Unscented Kalman Filter (UKF). The algorithm proves to be effective if applied to this class of vehicles and allows to achieve a satisfying accuracy improvement compared to standard navigation algorithms. The proposed strategy has been experimentally validated in suitable sea tests performed near the Cala Minnola wreck (Levanzo, Aegadian Islands, Sicily, Italy). The vehicles involved are the Typhoon AUVs, developed and built by the Department of Industrial Engineering of the University of Florence during the THESAURUS Tuscany Region project and the European ARROWS project for exploration and surveillance of underwater archaeological sites. The proposed algorithm has been implemented online on the AUVs and tested. The validation of the proposed strategy provided accurate results in estimating the vehicle dynamic behavior, better than those obtained through standard navigation algorithms.