• DocumentCode
    2905831
  • Title

    Coverage control of autonomous vehicles for oil spill cleaning in dynamic and uncertain environments

  • Author

    Xin Jin ; Ray, Avik

  • Author_Institution
    Dept. of Mech. Eng., Pennsylvania State Univ., University Park, PA, USA
  • fYear
    2013
  • fDate
    17-19 June 2013
  • Firstpage
    2594
  • Lastpage
    2599
  • Abstract
    In the context of oil spill cleaning by autonomous vehicles in dynamic and uncertain environments, this paper presents a multi-resolution algorithm that seamlessly integrates the concepts of local navigation and global navigation based on the sensory information; the objective here is to enable adaptive decision-making and online replanning of paths. The proposed algorithm provides a complete coverage of the search area for cleanup of the oil spills and does not suffer from the problem of having local minima, which is commonly encountered in potential-field-based methods. The efficacy of the algorithm is tested on a high-fidelity Player/Stage simulator for oil spill cleaning in a harbor, where the underlying oil weathering process is modeled as 2D random-walk particle tracking.
  • Keywords
    autonomous underwater vehicles; cleaning; decision making; navigation; oil pollution; path planning; water pollution control; 2D random-walk particle tracking; adaptive decision-making; autonomous vehicles; coverage control; dynamic environments; global navigation; high-fidelity player-stage simulator; local minima; local navigation; multiresolution algorithm; oil spill cleaning; oil weathering process; online path replanning; potential-field-based methods; sensory information; uncertain environments; Cleaning; Heuristic algorithms; Mobile robots; Navigation; Vectors; Vehicle dynamics; Vehicles; autonomous agents; coverage control; oil spill; path planning; uncertain environment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2013
  • Conference_Location
    Washington, DC
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-0177-7
  • Type

    conf

  • DOI
    10.1109/ACC.2013.6580225
  • Filename
    6580225