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Increasing the Autonomy Levels for Underwater Intervention Missions by Using Learning and Probabilistic Techniques

  • Jorge Sales
  • Luís Santos
  • Pedro J. Sanz
  • Jorge Dias
  • J. C. García
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

This paper represents research in progress in autonomous manipulation for underwater intervention missions within the context of the GRASPER project. This project focuses on developing manipulation skills for an Autonomous Underwater Vehicle (AUV). Current research in underwater robotics intends to increase autonomy for all kinds of robotic intervention operations that require physical interaction. Very few underwater systems have the capacity to carry out intervention without any kind of umbilical cables for tele-operating the actions. This article aims to investigate new approaches to follow with the aforementioned challenges, with the inclusion of learning and probabilistic techniques to increase the autonomy levels of an underwater manipulation system. With this goal, a collaboration research action has been established between the IRS-Lab at UJI (Spain), as experts in the underwater robotic manipulation domain, and the Institute of Systems and Robotics from University of Coimbra (Portugal), experts in learning by interaction within a robotic manipulation context.

Keywords

Underwater Autonomous Intervention Bayesian Learning Dynamic Bayesian Network UWSim underwater realistic simulator 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jorge Sales
    • 1
  • Luís Santos
    • 2
  • Pedro J. Sanz
    • 1
  • Jorge Dias
    • 2
    • 3
  • J. C. García
    • 1
  1. 1.Computer Science and Engineering Dept.University of Jaume-ICastellónSpain
  2. 2.Institute of Systems and Robotics, Department of Electrical and, Computer EngineeringUniversity of CoimbraCoimbraPortugal
  3. 3.Robotics InstituteKhalifa UniversityAbu DhabiUAE

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