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 experimental comparison of deep learning strategies for AUV navigation in DVL-denied environments

Accurate and robust navigation and localisation systems are critical for Autonomous Underwater Vehicles (AUVs) in order to perform missions in challenging environments. However, since the Global Positioning System (GPS) is not available in the underwater domain, the localisation task is commonly fulfilled by integrating direct linear speed readings provided by a Doppler Velocity Log (DVL) over time. As a consequence, DVL failures or fallacies and DVL-denied environments may arise as unexpected causes for severe malfunctions of the whole navigation system. Motivated by these considerations and the outstanding performance of Deep Neural Networks (DNNs) in supervised regression problems, a Deep Learning (DL) -based approach has been developed to estimate the vehicle’s body-frame velocity, without canonically employing DVL measurements, in a Dead-Reckoning (DR) navigation strategy. In particular, this work will describe the whole framework, starting from the data gathered by the AUVs of the National Oceanography Centre (NOC) during different field campaigns, through to the data pre-processing and the inference of the predicted velocity. Finally, a comprehensive offline comparison between different DL-based models is presented to assess the validity of the proposed approach