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A Study on Cost Function of Distributed Stochastic Search Algorithm for Ship Collision Avoidance

선박 간 충돌 방지를 위한 분산 확률 탐색 알고리즘의 비용 함수에 관한 연구

  • Kim, Donggyun (Graduate School of Maritime Sciences, Kobe University)
  • 김동균 (고베대학교 해사과학대학원)
  • Received : 2019.03.04
  • Accepted : 2019.04.26
  • Published : 2019.04.30

Abstract

When using a distributed system, it is very important to know the intention of a target ship in order to prevent collisions. The action taken by a certain ship for collision avoidance and the action of the target ship it intends to avoid influence each other. However, it is difficult to establish a collision avoidance plan in consideration of multiple-ship situations for this reason. To solve this problem, a Distributed Stochastic Search Algorithm (DSSA) has been proposed. A DSSA searches for a course that can most reduce cost through repeated information exchange with target ships, and then indicates whether the current course should be maintained or a new course should be chosen according to probability and constraints. However, it has not been proven how the parameters used in DSSA affect collision avoidance actions. Therefore, in this paper, I have investigated the effect of the parameters and weight factors of DSSA. Experiments were conducted by combining parameters (time window, safe domain, detection range) and weight factors for encounters of two ships in head-on, crossing, and overtaking situations. A total of 24,000 experiments were conducted: 8,000 iterations for each situation. As a result, no collision occurred in any experiment conducted using DSSA. Costs have been shown to increase if a ship gives a large weight to its destination, i.e., takes selfish behavior. The more lasting the expected position of the target ship, the smaller the sailing distance and the number of message exchanges. The larger the detection range, the safer the interaction.

충돌 피항 동작은 선박 간 끊임없이 영향을 주고받는다. 특히 다수의 선박이 조우하는 경우, 상대 선박의 피항 의도를 파악하고 서로에게 얼마나 영향을 미치는 지를 파악하는 것은 어려운 일이다. 이를 위해 분산 확률 탐색 알고리즘이 제안되었다. 분산 확률 탐색 알고리즘은 이웃 선박과 반복적인 메시지 교환을 통해 비용을 가장 크게 낮출 수 있는 코스를 탐색 후 확률과 제한 조건에 따라 기존의 코스를 유지할지 아니면 새로운 코스를 선택할지를 결정한다. 그러나 분산 확률 탐색 알고리즘에 사용된 파라미터가 충돌 피항에 어떠한 영향을 미치는지 증명되지 않았다. 본 논문에서는 분산 확률 탐색 알고리즘의 파라미터와 가중치가 충돌 피항에 어떠한 영향을 미치는지 분석하였다. 또한 타선과의 피항 거리를 조절하기 위한 충격 흡수 영역을 소개한다. 실험 방법은 두 선박이 조우할 수 있는 세 가지 상황, 즉 정면에서 조우하는 상황, 횡단하는 상황, 추월하는 상황에 파라미터와 가중치의 변수들을 조합하여 실험을 진행하였다. 각 상황 당 8,000회, 총 24,000회의 실험이 진행되었다. 실험 결과 모든 실험에서 한 건의 충돌도 발생하지 않았다. 선박이 목적지에 큰 가중치를 줄 경우, 즉 이기적인 행동을 할 경우, 비용은 증가함을 보였다. 타선의 움직임을 더 길게 예측할수록 항행 거리, 메시지 교환 횟수는 작아지는 경향을 보였다.

Keywords

References

  1. Coldwell, T. G.(1983), Marine Traffic Behaviour in Restricted Waters, The Journal of Navigation, Vol. 36, No. 3, pp. 430-444. https://doi.org/10.1017/S0373463300039783
  2. COLREGS(1972)(with amendments adopted from December 2009), Convention on the International Regulations for Preventing Collisions at Sea. International Maritime Organization, London.
  3. Fujii, Y. and K. Tanaka(1971), Traffic Capacity, The Journal of Navigation, Vol. 24, No. 4, pp. 543-552. https://doi.org/10.1017/S0373463300022384
  4. Glover, F.(1989), Tabu Search-Part I. ORSA, Journal on Computing, Vol. 1, No. 3, pp. 190-206. https://doi.org/10.1287/ijoc.1.3.190
  5. 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
  6. Hansen, M. G., T. K. Jensen, T. L. Schioler, K. Melchild, F. M. Rasmussen and F. Ennemark(2013), Empirical Ship Domain based on AIS Data, The Journal of Navigation, Vol. 66, No. 6, pp. 931-940. https://doi.org/10.1017/S0373463313000489
  7. Kim, D., K. Hirayama and G. Park(2014), Collision Avoidance in 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
  8. 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
  9. Kim, D., K. Hirayama and T. Okimoto(2017a), Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations, The Journal of Navigation, Vol. 70, No. 4, pp. 699-718. https://doi.org/10.1017/S037346331700008X
  10. Kim, J. K., S. W. Kim and Y. S. Lee(2017b), A Study on the Traffic Patterns of Dangerous Goods Carriers in Busan North and Gamcheon Port, Journal of the Korean Society of Marine Environment & Safety, Vol. 23, No. 1, pp. 1-16. https://doi.org/10.7837/kosomes.2017.23.1.001
  11. Kim, S. C. and Y. M. Kwon(2017), A Review of Proximity Assessment Measurements According to Fairway Patterns and Ship Size, Journal of the Korean Society of Marine Environment & Safety, Vol. 23, No. 7, pp. 783-790. https://doi.org/10.7837/kosomes.2017.23.7.783
  12. Lazarowska, A.(2015), Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation, The Journal of Navigation, Vol. 68, No. 2, pp. 291-307. https://doi.org/10.1017/S0373463314000708
  13. Lee, M. K., Y. S. Park and W. S. Kang(2019), A Study on Construction of Collision Prevention Algorithm for Small Vessel Using WAVE Communication System, Journal of the Korean Society of Marine Environment & Safety, Vol. 25, No. 1, pp. 1-8. https://doi.org/10.7837/kosomes.2019.25.1.001
  14. Lee, S., K. Kwon and J. Joh(2004), A Fuzzy Logic for Autonomous Navigation of Marine Vehicles Satisfying COLREG Guidelines, International Journal of Control, Automation and Systems, Vol. 2, No. 2, pp. 171-181.
  15. Montewka, J., S. Ehlers, F. Goerlandt, T. Hinz, K. Tabri and P. Kujala(2014), A framework for risk assessment for maritime transportation systems-A case study for open sea collisions involving RoPax vessels, Reliability Engineering and System Safety, Vol. 124, pp. 142-157. https://doi.org/10.1016/j.ress.2013.11.014
  16. Szlapczynski, R.(2006), A unified measure of collision risk derived from the concept of a ship domain, The Journal of Navigation, Vol. 59, pp. 477-490. https://doi.org/10.1017/S0373463306003833
  17. Szlapczynski, R.(2007), Determining the Optimal course Alteration Manoeuvre in a Multi-Target Encounter Situation for a Given Ship domain Model, Annual of Navigation, Vol. 12, pp. 75-85.
  18. Szlapczynski, R.(2008), A New Method of Planning Collision Avoidance Manoeuvres for Multi-Target Encounter Situations, The Journal of Navigation, Vol. 61, pp. 307-321. https://doi.org/10.1017/S0373463307004638
  19. Szlapczynski, R. and J. Szlapczynska(2015), A simulative Comparison of Ship Domains and Their Polygonal Approximations, The International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, pp. 136-141.
  20. Szlapczynski, R.(2011), Evolutionary Sets of Safe Ship Trajectories: A New Approach to Collision Avoidance, The Journal of Navigation, Vol. 64, No. 1, pp. 169-181. https://doi.org/10.1017/S0373463310000238
  21. Xu, Q. and N. Wang(2014), A Survey on Ship Collision Risk Evaluation, Scientific Journal on Traffic and Transportation Research, Vol. 26, No. 6, pp. 475-486.
  22. Yoo, S. L., D. B. Kim and J. Y. Jeong(2016), A Study on the Establishment of Specific Traffic Safety Areas at Pyeongtaek Port, Journal of the Korean Society of Marine Environment & Safety, Vol. 22, No. 6, pp. 660-670. https://doi.org/10.7837/kosomes.2016.22.6.660
  23. Zhang, W., G. Wang, Z. Xing and L. Wittenburg(2005), Distributed Stochastic Search and Distributed Breakout: Properties, Comparison and Applications to Constraint Optimization Problems in Sensor Networks, Artificial Intelligence, Vol. 161, No. 1-2, pp. 55-87. https://doi.org/10.1016/j.artint.2004.10.004