@article {2847, title = {An Attitude Estimation Algorithm for Mobile Robots under Unknown Magnetic Disturbances}, journal = {IEEE/ASME TRANSACTIONS ON MECHATRONICS}, volume = {21}, year = {2016}, pages = {1900-1911}, chapter = {1900}, abstract = {
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{\textquoteright}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.
}, keywords = {Robotics}, doi = {10.1109/TMECH.2016.2559941}, url = {http://ieeexplore.ieee.org/document/7460980/}, author = {R. Costanzi and F. Fanelli and N. Monni and A. Ridolfi and B. Allotta} } @article {2845, title = {A new AUV navigation system exploiting unscented Kalman filter}, journal = {OCEAN ENGINEERING}, volume = {113}, year = {2016}, pages = {121-132}, 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.

}, keywords = {Robotics}, doi = {10.1016/j.oceaneng.2015.12.058}, author = {B. Allotta and A. Caiti and R. Costanzi and F. Fanelli and D. Fenucci and E. Meli and A. Ridolfi} }