A new AUV navigation system exploiting unscented Kalman filter

Abstract

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. A precise and reliable navigation system is indeed fundamental to AUVs: the Global Positioning System (GPS) signal is not available underwater, thus making it very hard to know the position of the vehicle in real-time. In this paper, the authors present an innovative navigation strategy specifically designed for AUVs, based on the Unscented Kalman Filter (UKF). The new algorithm proves to be effective if applied to this class of vehicles and allows us to achieve a satisfying accuracy improvement compared to standard navigation algorithms. The proposed strategy has been experimentally validated using the navigation data acquired in suitable sea tests performed in Biograd Na Moru (Croatia) in the framework of the FP7 European ARROWS project tests performed during the Breaking the Surface 2014 (BtS 2014) workshop. The vehicles involved are the two Typhoon AUVs, developed and built by the Department of Industrial Engineering of the University of Florence within the THESAURUS Tuscany Region project for exploration and surveillance of underwater archaeological sites. The experiment, described in the paper, was performed to preliminary test the cooperative navigation between these AUVs. The new algorithm has been initially tested offline, and the validation of the proposed strategy provided accurate results in estimating the vehicle dynamic behaviour

Underwater Vehicles attitude estimation in presence of magnetic disturbances

“…The performances of the proposed filter have been tested on the Typhoon AUV [8], [9], [10], [11], developed and built by the MDM Lab of the University of Florence (visible in Fig. 1 during a sea trial) in the framework of the THESAURUS 1 and the ARROWS 2 projects.…”

“…[5], [6], [7]), a calibration technique in the case of constrained rotation of the sensor is presented. Experimental results, carried out with the Typhoon AUV [8], [9], [10], [11] the Tuscany Region THESAURUS project and the FP7 European ARROWS project, are presented. The achieved results, compared to those obtained through commercial software integrated on the Typhoon IMU, demonstrate to be satisfying, confirming that the proposed strategy is able to identify external disturbances occurrences and to reject their effects in real-time.…”

Augmented Virtuality for Coastal Management: A Holistic Use of In Situ and Remote Sensing for Large Scale Definition of Coastal Dynamics

“…• Estimation based on proprioceptive data: methods, mainly based on the Kalman filter approach, for the fusion of proprioceptive sensor (e.g., DVL-Doppler Velocity Log, AHRS-Attitude and Heading Reference Systems) signals and dynamic evolution knowledge, have been investigated and experimentally validated [58].…”

An Acoustic Network Navigation System

“…Alternative approaches that overcome these limitations have been proposed, and the reader is referred, for instance to, Bahr, Leonard, and Fallon (); Allotta et al. (); Paull, Seto, and Leonard (); Walls, Cunningham, and Eustice () and references therein.…”