Collision Avoidance in Multiple-Ship Situations by Distributed Local Search

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Abstract

<p>As vital transportation carriers in trade, ships have the advantage of stability, economy, and bulk capacity over airplanes, trucks, and trains. Even so, their loss and cost due to collisions and other accidents exceed those of any other mode of transportation. To prevent ship collisions many ways have been suggested, e.g., the 1972 COLREGs which is the regulation for preventing collision between ships. Technologically speaking, many related studies have been conducted. The term "Ship domain" involves that area surrounding a ship that the navigator wants to keep other ships clear of. Ship domain alone is not sufficient, however, for enabling one or more ships to simultaneously determine the collision risk for all of the ships concerned. Fuzzy theory is useful in helping ships avoid collision in that fuzzy theory may define whether collision risk is based on distance to closest point of approach, time to closest point of approach, or relative bearing – algorithms that are difficult to apply to more than one ships at one time. The main purpose of this study is thus to reduce collision risk among multiple ships using a distributed local search algorithm (DLSA). By exchanging information on, for example, next-intended courses within a certain area among ships, ships having the maximum reduction in collision risk change courses simultaneously until all ships approach a destination without collision. In this paper, we introduce distributed local search and explain how it works using examples. We conducted experiments to test distributed local search performance for certain instances of ship collision avoidance. Experiments results showed that in most cases, our proposal applies well in ship collision avoidance among multiple ships.</p>

Journal

  • Journal of Advanced Computational Intelligence and Intelligent Informatics

    Journal of Advanced Computational Intelligence and Intelligent Informatics 18(5), 839-848, 2014

    Fuji Technology Press Ltd.

Codes

  • NII Article ID (NAID)
    130007673182
  • Text Lang
    ENG
  • ISSN
    1343-0130
  • NDL Article ID
    025788399
  • NDL Call No.
    Z78-A599
  • Data Source
    NDL  J-STAGE 
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