02315nas a2200181 4500008004100000245009100041210006900132300001400201490000700215520177000222653001301992100001702005700001602022700001402038700001602052700001602068856004902084 2016 eng d00aAn Attitude Estimation Algorithm for Mobile Robots under Unknown Magnetic Disturbances0 aAttitude Estimation Algorithm for Mobile Robots under Unknown Ma a1900-19110 v213 a
Attitude estimation is a crucial aspect for navigation and motion control of autonomous vehicles. This concept is particularly true in the case of unavailability of localization sensors when navigation and control rely on dead reckoning strategies; in this case, indeed, the orientation estimate is also used along with speed measurements to update the position estimate. Among the different approaches proposed in the literature, the de facto state of the art in this field is represented by nonlinear complementary filters: they fuse the measurements of angular rate obtained through gyroscopes, and a measurement of gravity and Earth’s magnetic field vectors respectively obtained through accelerometers and magnetometers. This paper is focused on an attitude estimation strategy for autonomous underwater vehicles (AUV). The proposed novelty includes the identification of some critical issues that arise when AUV attitude estimation algorithms are applied in practice.
They are mainly due to the use of low-accuracy low-cost microelectromechanical systems (MEMS) sensors and on different sources of magnetic disturbances. Some strategies to overcome the identified issues are proposed, including the integration of a single-axis fiber optic gyroscope (FOG) that ensures a considerable performance improvement with a moderate cost increase. The proposed strategies for detection of issues and sensor fusion have been experimentally tested and validated in a real application scenario estimating the attitude of an AUV performing a lawn mower path.
The expected performance improvement is confirmed; the obtained results are described and analyzed in this paper.
10aRobotics1 aCostanzi, R.1 aFanelli, F.1 aMonni, N.1 aRidolfi, A.1 aAllotta, B. uhttp://ieeexplore.ieee.org/document/7460980/01826nas a2200241 4500008004100000022001400041245011700055210006900172260001200241300001600253490000700269520111300276653001301389100001601402700001301418700001701431700001801448700001501466700001401481700001301495700001601508856006001524 2016 eng d a0929-559300aCooperative navigation of AUVs via acoustic communication networking: field experience with the Typhoon vehicles0 aCooperative navigation of AUVs via acoustic communication networ c10/2016 a1229–12440 v403 a

A cooperative navigation procedure for a team of autonomous underwater vehicles (AUVs) is described and validated on experimental data. The procedure relies on acoustic communication networking among the AUVs and/or fixed acoustic nodes, and it is suitable as a low-cost solution for team navigation. Embedding the acoustic localization measurements in the communication scheme causes delays and sometimes loss of acoustic data, depending on acoustic propagation conditions. Despite this drawback, the results obtained show that on-board localization estimates have an error of the order of few meters, improving the overall navigation performance and leading the system towards long-term autonomy in terms of operating mission time, without the need of periodic resurfacings dedicated to reset the estimation error. The data were collected during the CommsNet ’13 experiment, led by the NATO Science and Technology Organization Center for Maritime Research and Experimentation (CMRE), and the Breaking The Surface ’14 workshop, organized by the University of Zagreb.

10aRobotics1 aAllotta, B.1 aCaiti, A1 aCostanzi, R.1 aDi Corato, F.1 aFenucci, D1 aMonni, N.1 aPugi, L.1 aRidolfi, A. uhttp://link.springer.com/journal/volumesAndIssues/1051402427nas a2200205 4500008004100000245006700041210006500108300001200173490000800185520180200193653001301995100001602008700001302024700001702037700001602054700001502070700001302085700001602098856010702114 2016 eng d00aA new AUV navigation system exploiting unscented Kalman filter0 anew AUV navigation system exploiting unscented Kalman filter a121-1320 v1133 a

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.

10aRobotics1 aAllotta, B.1 aCaiti, A1 aCostanzi, R.1 aFanelli, F.1 aFenucci, D1 aMeli, E.1 aRidolfi, A. uhttp://118699.obes.asia/publications/new-auv-navigation-system-exploiting-unscented-kalman-filter.html02286nas a2200241 4500008004100000245009300041210006900134260001200203300001200215490000700227520158600234653001301820100001601833700001301849700001501862700001701877700001801894700001701912700001501929700001301944700001601957856007101973 2016 eng d00aAn unscented Kalman filter based navigation algorithm for autonomous underwater vehicles0 aunscented Kalman filter based navigation algorithm for autonomou c11/2016 a185-1950 v393 a

Robust and performing navigation systems for Autonomous Underwater Vehicles (AUVs) play a discriminant role towards the success of complex underwater missions involving one or more AUVs. The quality of the filtering algorithm for the estimation of the AUV navigation state strongly affects the performance of the overall system. In this paper, the authors present a comparison between the Extended Kalman Filter (EKF) approach, classically used in the field of underwater robotics and an Unscented Kalman Filter (UKF). The comparison results to be significant as the two strategies of filtering are based on the same process and sensors models. The UKF-based approach, here adapted to the AUV case, demonstrates to be a good trade-off between estimation accuracy and computational load. UKF has not yet been extensively used in practical underwater applications, even if it turns out to be quite promising. The proposed results rely on the data acquired during a sea mission performed by one of the two Typhoon class vehicles involved in the NATO CommsNet13 experiment (held in September 2013). As ground truth for performance evaluation and comparison, performed offline, position measurements obtained through Ultra-Short BaseLine (USBL) fixes are used. The result analysis leads to identify both the strategies as effective for the purpose of being included in the control loop of an AUV. The UKF approach demonstrates higher performance encouraging its implementation as a more suitable navigation algorithm even if, up to now, it is still not used much in this field.

10aRobotics1 aAllotta, B.1 aCaiti, A1 aChisci, L.1 aCostanzi, R.1 aDi Corato, F.1 aFantacci, D.1 aFenucci, D1 aMeli, E.1 aRidolfi, A. uhttp://www.sciencedirect.com/science/article/pii/S095741581630037X