Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
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A Study on Intention Exchange-based Ship Collision Avoidance by Changing the Safety Domain

  • Kim, Donggyun (Graduate School of Maritime Sciences, Kobe University)
  • Received : 2019.03.27
  • Accepted : 2019.05.28
  • Published : 2019.05.31
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Abstract

Even if only two ships are encountered, a collision may occur due to the mistaken judgment of the positional relationship. In other words, if an officer does not know a target ship's intention, there is always a risk of collision. In this paper, the experiments are conducted to investigate how the intention affects the action of collision avoidance in cooperative and non-cooperative situations. In non-cooperative situation, each ship chooses a course that minimizes costs based on the current situation. That is, it always performs a selfish selection. In a cooperative situation, the information is exchanged with a target ship and a course is selected based on this information. Each ship uses the Distributed Stochastic Search Algorithm so that a next-intended course can be selected by a certain probability and determines the course. In the experimental method, four virtual ships are set up to analyze the action of collision avoidance. Then, using the actual AIS data of eight ships in the strait of Dover, I compared and analyzed the action of collision avoidance in cooperative and non-cooperative situations. As a result of the experiment, the ships showed smooth trajectories in the cooperative situation, but the ship in the non-cooperative situation made frequent big changes to avoid a collision. In the case of the experiment using four ships, there was no collision in the cooperative situation regardless of the size of the safety domain, but a collision occurred between the ships when the size of the safety domain increased in cases of non-cooperation. In the case of experiments using eight ships, it was found that there are optimal parameters for collision avoidance. Also, it was possible to grasp the variation of the sailing distance and the costs according to the combination of the parameters, and it was confirmed that the setting of the parameters can have a great influence on collision avoidance among ships.

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References

  1. COLREG.(1972), Convention on the International Regulations for Preventing Collisions at Sea. International Maritime Organization, London.
  2. Fuiji, Y. and K. Tanaka(1971), Traffic Capacity, The Journal of Navigation, Vol. 24, No. 4, pp. 543-552. https://doi.org/10.1017/S0373463300022384
  3. Goodwin, E. M.(1975), A Statistical Study of Ship Domains, The Journal of Navigation, Vol. 28, No. 3, pp. 328-344. https://doi.org/10.1017/S0373463300041230
  4. Kim, D., K. Hirayama and G. Park(2014), Collision Avoidance Multiple-Ship Situations by Distributed Local Search. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 18, No. 5, pp. 839-848. https://doi.org/10.20965/jaciii.2014.p0839
  5. Kim, D., K. Hirayama and T. Okimoto(2015), Ship Collision Avoidance by Distributed Tabu Search, The International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, No. 1, pp. 23-29. https://doi.org/10.12716/1001.09.01.03
  6. Kim, D., K. Hirayama and T. Okimoto(2017), Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations, The Journal of Navigation, Vol. 70, pp. 699-718. https://doi.org/10.1017/S037346331700008X
  7. Szlapczynski, R.(2006), A Unified Measure of Collision Risk derived from the Concept of a Ship Domain, The Journal of Navigation, Vol. 59, No. 3, pp. 477-490. https://doi.org/10.1017/S0373463306003833
  8. Tsou, M. and C. Hsueh(2010), The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm, Journal of Marine Science and Technology, Vol. 18, No. 5, pp. 746-756.
  9. Tsou, M., S. Kao and C. Su(2010), Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts, The Journal of Navigation, Vol. 63, No. 1, pp. 167-182. https://doi.org/10.1017/S037346330999021X
  10. Zhang, W., G. Wang and L. Wittenburg(2002), Distributed Stochastic Search for Constraint Satisfaction and Optimization: Parallelism, Phase Transitions and Performance: Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI-2002), pp, 53-59.
  11. Zhang, W., G. Wang, Z. Xing and L. Wittenburg(2005), Distributed Stochastic Search and Distributed Breakout: Properties, Comparison and Applications to Contraint Optimization Problems in Sensor Networks. Artificial Intelligence, Vol. 161, pp. 1-2, pp. 55-87. https://doi.org/10.1016/j.artint.2004.10.004