David Ribas

Girona 500 AUV: From Survey to Intervention

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 tasks.

SLAM using an Imaging Sonar for Partially Structured Underwater Environments

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: (I) the full 360deg 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

Visual inspection of hydroelectric dams using an autonomous underwater vehicle

This paper presents an automated solution to the visual inspection problem of hydroelectric dams. A small autonomous underwater vehicle, controllable in four degrees of freedom (surge, sway, heave, and yaw), is used for autonomously exploring the wall of a dam. The robot is easily programmed using a mission control language. Missions are executed by an intelligent control architecture that guides the robot to follow a predefined path surveying the wall. During the mission, the robot gathers onboard navigation data synchronized with optical imagery, sonar, and absolute navigation data, obtained from a moored buoy equipped with an ultra-short baseline system. After the mission, the images of the wall are used to build a photomosaic of the inspected area. First, image features are matched over the image sequence. Then, navigation data and interimage correspondences are optimized together using bundle adjustment techniques. Thus, a georeferenced globally aligned set of images is obtained. Finally, a blending algorithm is used to obtain smooth seam transitions among the different images that constitute the mosaic, compensating for light artifacts and improving the visual perception of the scene.

Neptune: a hil simulator for multiple UUVs

This paper overviews the field of graphical simulators used for AUV development, presents the taxonomy of these applications and proposes a classification. It also presents Neptune, a multivehicle, real-time, graphical simulator based on OpenGL that allows hardware in the loop simulations.

Grasping for the Seabed: Developing a New Underwater Robot Arm for Shallow-Water Intervention

A new underwater robot arm was developed through intensive cooperation between different academic institutions and an industrial company. The manipulator, which was initially designed to be teleoperated, was adapted for our autonomy needs. Its dimensions and weight were reduced, and its kinematic model was developed so that autonomous control can be performed with it. We compare several commercially available underwater manipulators and describe the development of the new one, from its initial configuration to its mechanical adaptation, modeling, control, and final assembly on an autonomous underwater vehicle (AUV). The feasibility and reliability of this arm is demonstrated in water tank conditions, where various innovative autonomous object-recovery operations are successfully performed, both in stand-alone operation and integrated in an AUV prototype.

Underwater SLAM for Structured Environments Using an Imaging Sonar

This book is a revised version of the doctoral dissertation presented by D. Ribas in the Department of Computer Engineering at the University of Girona. The main purpose of this work is to present different techniques developed with the objective of providing a solution to the navigation problem for Autonomous Underwater Vehicles (AUVs) operating in structured environments, with special attention to localization techniques but, particularly, to the application of SLAM (Simultaneous Localization And Mapping) techniques as a self-contained system which requires neither previous knowledge of the scenario nor the use of absolute positioning systems like GPS, LBL or USBL. This book also presents techniques for feature extraction capable of dealing with the particular complexities of a mechanically scanned imaging sonar. The approaches described in this book are designed for use in structured environments like those present in many industrial scenarios, specifically for scenarios containing manmade structures in the form of rectilinear walls like those met in harbours, breakwaters, marinas, canal systems, etc. Although most of the previous work done in this field focuses on open sea and coastal applications, obtaining an accurate positioning in such scenarios would increase AUVs capabilities notably, allowing to perform autonomously tasks such as inspection of underwater structures, surveillance of marine installations and even enabling autonomous harbour leaving and returning operations.

Underwater SLAM in a marina environment

This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas or marine platforms. A mechanical scanning imaging sonar is used to obtain information about the location of planar structures present in such environments. A modified version of the Hough transform has been developed to extract line features, together with their uncertainty, from the continuous sonar dataflow. The information obtained is incorporated into a feature-based SLAM algorithm running an Extended Kalman Filter (EKF). Simultaneously, the AUVs position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach.

EKF-SLAM for AUV navigation under probabilistic sonar scan-matching

This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach.

Fourier-based Registration for Robust Forward-looking Sonar Mosaicing in Low-visibility Underwater Environments

Vehicle operations in underwater environments are often compromised by poor visibility conditions. For instance, the perception range of optical devices is heavily constrained in turbid waters, thus complicating navigation and mapping tasks in environments such as harbors, bays, or rivers. A new generation of high-definition forward-looking sonars providing acoustic imagery at high frame rates has recently emerged as a promising alternative for working under these challenging conditions. However, the characteristics of the sonar data introduce difficulties in image registration, a key step in mosaicing and motion estimation applications. In this work, we propose the use of a Fourier-based registration technique capable of handling the low resolution, noise, and artifacts associated with sonar image formation. When compared to a state-of-the art region-based technique, our approach shows superior performance in the alignment of both consecutive and nonconsecutive views as well as higher robustness in featureless environments. The method is used to compute pose constraints between sonar frames that, integrated inside a global alignment framework, enable the rendering of consistent acoustic mosaics with high detail and increased resolution. An extensive experimental section is reported showing results in relevant field applications, such as ship hull inspection and harbor mapping.

I-AUV Mechatronics Integration for the TRIDENT FP7 Project

Autonomous underwater vehicles (AUVs) are routinely used to survey areas of interest in seas and oceans all over the world. However, those operations requiring intervention capabilities are still reserved to manned submersibles or remotely operated vehicles (ROVs). In the recent years, few research projects have demonstrated the viability of a new type of submersible, the intervention AUV (I-AUV), which can perform underwater missions involving manipulations in a completely autonomous way. The EU FP7 TRIDENT project is one of the most recent examples of such technological concept. This paper describes the different mechatronic components that constitute the I-AUV developed for the TRIDENT project, their hardware and software integration, and the performance of the vehicle during the project trials.