This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasksManuscript received April 1, 2011; revised August 5, 2011; accepted October 7, 2011. Date of publication November 30, 2011; date of current version January 9, 2012. Recommended by Guest Editor W. Kirkwood. This work was supported in part by the TRIDENT EU FP7-Project under Grant ICT-248497, in part by the Marie Curie PERG-GA-2010-276778 (Surf3DSLAM), and in part by the Spanish Government under the projects DPI2008-06548-C03 and CTM2010-15216/MA
This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas, and marine platforms. A mechanically scanned imaging sonar is used to obtain information about the location of vertical planar structures present in such environments. A robust voting algorithm has been developed to extract line features, together with their uncertainty, from the continuous sonar data flow. The obtained information is incorporated into a feature-based simultaneous localization and mapping (SLAM) algorithm running an extended Kalman filter. Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Moreover, a procedure to build and maintain a sequence of local maps and to posteriorly recover the full global map has been adapted for the application presented. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach. C 2008 Wiley Periodicals, Inc.
Abstract-In this paper we describe a system for underwater navigation with AUVs in partially structured environments, such as dams, ports or marine platforms. An imaging sonar is used to obtain information about the location of planar structures present in such environments. This information is incorporated into a feature-based SLAM algorithm in a two step process: (1) the full 360• sonar scan is undistorted (to compensate for vehicle motion), thresholded and segmented to determine which measurements correspond to planar environment features and which should be ignored; and (2) SLAM proceeds once the data association is obtained: both the vehicle motion and the measurements whose correct association has been previously determined are incorporated in the SLAM algorithm. This two step delayed SLAM process allows to robustly determine the feature and vehicle locations in the presence of large amounts of spurious or unrelated measurements that might correspond to boats, rocks, etc. Preliminary experiments show the viability of the proposed approach.
In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves' horizontal and vertical surfaces. Although the caves' spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and map- * The author can be also reached at Computer Vision and Robotics Institute, Universitat de Girona, 17003, Girona, Spain, amallios@eia.udg.edu.ping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.
This paper proposes the use of path-planning algorithms for hovering autonomous underwater vehicles (AUVs) in applications where the robot needs to adapt online its trajectory for inspection or safety purposes. In particular, it proposes the platform Sparus II AUV and a set of planning algorithms to conduct these new AUV capabilities. These algorithms generate trajectories under motion constraints, which can be followed without deviations, to ensure the safety even when passing close to obstacles. View planning algorithms are also combined to decide the movements to be executed to discover the unexplored seabed or target, and to cover it with a camera or sonar. Online mapping with profiling sonars and online planning with fast sampling-based algorithms allow the execution of missions without any previous knowledge of the 3-D shape of the environment. Real 2-D results in an artificial harbor structure and simulated natural rocky canyon demonstrate the feasibility of the approach for avoiding or inspecting the underwater environment. These new AUV capabilities can be used to acquire images of the environment that can be used to inspect and map the habitat. Index Terms-Autonomous underwater vehicle (AUV), hovering AUV, online path planning and view planning (VP). I. INTRODUCTION C OMMERCIAL autonomous underwater vehicles (AUVs) are mainly conceived to surveying applications in which large areas must be covered and the vehicle follows safe paths at safe altitudes. New advances in sonar technology, image processing, mapping, and robotics will allow more complex missions, in which the AUV will be able to navigate at a closer distance from the seabed, it will react to the 3-D shape of the environment, and it will even perform some autonomous intervention tasks. In this context, the Underwater Robotics Research Centre of the University of Girona has been developing several AUV prototypes during more than 20 years to achieve these new capabilities. The Sparus II AUV [1] [see Fig. 1(a)] is one of them, and was conceived as a hovering AUV for surveying and inspection applications. The vehicle was developed in 2013 and during four years, many experiments have been carried out,
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