• DocumentCode
    3709066
  • Title

    A Micro-Aerial platform for vessel visual inspection based on supervised autonomy

  • Author

    Francisco Bonnin-Pascual;Alberto Ortiz;Emilio Garcia-Fidalgo;Joan P. Company

  • Author_Institution
    Department of Mathematics and Computer Science, University of the Balearic Islands, 07122 Palma de Mallorca, Spain
  • fYear
    2015
  • Firstpage
    46
  • Lastpage
    52
  • Abstract
    Seagoing vessels have to undergo regular visual inspections in order to detect the typical defective situations affecting metallic structures, such as cracks and corrosion. These inspections are currently performed by ship surveyors manually at a great cost. To make ship inspections safer and more cost-efficient, this paper presents a Micro-Aerial Vehicle (MAV) intended for visual inspection and based on supervised autonomy. On the one hand, the vehicle is equipped with a vision system that effectively teleports the surveyor from the base station to the areas of the hull that need inspection. On the other hand, the MAV is the result of a complete redesign of a visual inspection-oriented aerial platform that we proposed some years ago, with the aim of introducing the surveyor in the control loop and, in this way, enlarge the range of inspection operations that can robustly be carried out. Another goal is to make the platform as usable as possible for a non-expert. All this has been accomplished by means of the definition of different autonomous functions, including obstacle detection and collision prevention, and extensive use of behavior-based high-level control. The results of some experiments conducted to assess both the performance and usability of the platform are discussed at the end of the paper.
  • Keywords
    "Inspection","Optical sensors","Vehicles","Robots","Visualization","Navigation"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on
  • Type

    conf

  • DOI
    10.1109/IROS.2015.7353353
  • Filename
    7353353