Development and testing of the propulsion system of MARTA AUV

This work deals with the design of the propulsion system of a modular AUV (Autonomous Underwater Vehicle). The authors describe the design methodologies and the testing devices used for the fast prototyping of MARTA (MARine Tool for Archaeology) AUV actuation system, including drivers, motors and propellers. In particular, the authors introduce the design criteria followed for the preliminary testing activities and the methodologies adopted for fast testing and prototyping of the proposed solutions. This is a quite important topic considering the high customization and the reliability required by this kind of applications

An IMU and USBL-aided buoy for underwater localization

Autonomous underwater navigation remains, as of today, a challenging task. The marine environment limits the number of sensors available for precise localization, hence Au- tonomous Underwater Vehicles (AUVs) usually rely on inertial and velocity sensors to obtain an estimate of their position either through dead reckoning or by means of more sophisticated navigation filters (such as Kalman filters and its extensions [1]). On the other hand, acoustic localization makes possible the determination of a reliable vehicles pose estimate exploiting suit- able acoustic modems [3]; such estimate can even be integrated within the navigation filter of the vehicle in order to increase its accuracy. In this paper, the authors discuss the development and the performance of an Ultra-Short BaseLine (USBL)-aided buoy to improve the localization of underwater vehicles. At first, the components and the physical realization of the buoy will be discussed; then, the procedure to compute the position of the target will be analyzed. The following part of the paper will be focused on the development of a recursive state estimation algorithm to process the measurements computed by the buoy; specifically, Extended Kalman Filter [4] has been adopted to deal with the nonlinearities of the sensors housed on the buoy. A validation of the measurement filtering through experimental tests is also proposed

An IMU and USBL-aided buoy for underwater localization

Autonomous underwater navigation remains, as of today, a challenging task. The marine environment limits the number of sensors available for precise localization, hence Au- tonomous Underwater Vehicles (AUVs) usually rely on inertial and velocity sensors to obtain an estimate of their position either through dead reckoning or by means of more sophisticated navigation filters (such as Kalman filters and its extensions [1]). On the other hand, acoustic localization makes possible the determination of a reliable vehicles pose estimate exploiting suit- able acoustic modems [3]; such estimate can even be integrated within the navigation filter of the vehicle in order to increase its accuracy. In this paper, the authors discuss the development and the performance of an Ultra-Short BaseLine (USBL)-aided buoy to improve the localization of underwater vehicles. At first, the components and the physical realization of the buoy will be discussed; then, the procedure to compute the position of the target will be analyzed. The following part of the paper will be focused on the development of a recursive state estimation algorithm to process the measurements computed by the buoy; specifically, Extended Kalman Filter [4] has been adopted to deal with the nonlinearities of the sensors housed on the buoy. A validation of the measurement filtering through experimental tests is also proposed

Piecewise planar underwater mosaicing

A commonly ignored problem in planar mosaics, yet often present in practice, is the selection of a reference homography reprojection frame where to attach the successive image frames of the mosaic. A bad choice for the reference frame can lead to severe distortions in the mosaic and can degenerate in incorrect configurations after some sequential frame concatenations. This problem is accentuated in uncontrolled underwater acquisition setups as those provided by AUVs or ROVs due to both the noisy trajectory of the acquisition vehicle - with roll and pitch shakes - and to the non-flat nature of the seabed which tends to break the planarity assumption implicit in the mosaic construction. These scenarios can also introduce other undesired effects, such as light variations between successive frames, scattering and attenuation, vignetting, flickering and noise. This paper proposes a novel mosaicing pipeline, also including a strategy to select the best reference homography in planar mosaics from video sequences which minimizes the distortions induced on each image by the mosaic homography itself. Moreover, a new non-linear color correction scheme is incorporated to handle strong color and luminosity variations among the mosaic frames. Experimental evaluation of the proposed method on real, challenging underwater video sequences shows the validity of the approach, providing clear and visually appealing mosaic

Development of a navigation algorithm for autonomous underwater vehicles

In this paper, the authors present an underwater navigation system for Autonomous Underwater Vehicles (AUVs) which exploits measurements from an Inertial Measurement Unit (IMU), a Pressure Sensor (PS) for depth and the Global Positioning System (GPS, used during periodic and dedicated resurfacings) and relies on either the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) for the state estimation. Both (EKF and UKF) navigation algorithms have been validated through experimental navigation data related to some sea tests performed in La Spezia (Italy) with one of Typhoon class vehicles during the NATO CommsNet13 experiment (held in September 2013) and through Ultra-Short BaseLine (USBL) fixes used as a reference (ground truth). Typhoon is an AUV designed by the Department of Industrial Engineering of the Florence University for exploration and surveillance of underwater archaeological sites in the framework of the Italian THESAURUS project and the European ARROWS project. The obtained results have demonstrated the effectiveness of both navigation algorithms and the superiority of the UKF (very suitable for AUV navigation and, up to now, still not used much in this field) without increasing the computational load (affordable for on-line on-board AUV implementation)

An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

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

Are diabetes and its medications risk factors for the development of COVID-19? Data from a population-based study in Sicily.

Background and aims: Diabetes mellitus (DM) has been associated with higher incidence of severe cases of COVID-19 in hospitalized patients, but it is unknown whether DM is a risk factor for the overall COVID-19 incidence. The aim of present study was to investigate whether there is an association of DM with COVID-19 prevalence and case fatality, and between different DM medications and risk for COVID-19 infection and death. Methods and results: retrospective observational study on all SARS-CoV-2 positive (SARS-CoV-2+) cases and deaths in Sicily up to 2020, May 14th. No difference in COVID-19 prevalence was found between people with and without DM (RR 0.92 [0.79-1.09]). Case fatality was significantly higher in SARS-CoV-2+ with DM (RR 4.5 [3.55-5.71]). No diabetes medication was associated with differences in risk for SARS-Cov2 infection. Conclusions: in Sicily, DM was not a risk factor for COVID-19 infection, whereas it was associated with a higher case fatality