AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Recent Progress in the RAUVI Project: A Reconfigurable Autonomous Underwater Vehicle for Intervention

Starting in January 2009, the RAUVI project is a three years coordinated research action funded by the Spanish Ministry of Research and Innovation. This paper shows the research evolution during the first half of RAUVI’s live, bearing in mind that the long term objective is to design and develop an underwater autonomous robot able to perceive the environment and, by means of a specific hand-arm system, perform autonomously simple intervention tasks in shallow waters.This research was partly supported by the European Commission’s Seventh Framework Programme FP7/2007- 2013 under grant agreement 248497 (TRIDENT Project), by Spanish Ministry of Research and Innovation DPI2008-06548- C03 (RAUVI Project), and by Fundació Caixa Castelló- Bancaixa P1-1B2009-50

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

RBPF-MSIS: Toward Rao-Blackwellized Particle Filter SLAM for Autonomous Underwater Vehicle With Slow Mechanical Scanning Imaging Sonar

Simultaneous localization and mapping (SLAM) has the potential to play a fundamental and significant role in achieving full autonomy for autonomous underwater vehicles (AUV). This article proposes a Rao-Blackwellized particle filter (RBPF) SLAM algorithm for an AUV equipped with a mechanically scanning imaging sonar (MSIS) that has a very slow scanning frequency. To tackle the issues of scan distortion and sonar data sparseness caused by the slow-sampling MSIS, the core of the algorithm is a carefully designed sliding window-based scan forming module. Then the formed scans are fed into the modified RBPF to build a consistent grid-based map thus localizing the AUV accurately. Extensive simulation and experiments are carried out to verify the proposed algorithm. The results show that the proposed algorithm outperforms existing ones in terms of the level of map consistency with the environment as well as the accuracy of pose estimation