Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Collision Avoidance Algorithm Based on Obstacle Zone by Target

Obstacle Zone by Target 기반 선박 충돌회피 알고리즘 개발에 관한 연구

  • Chan-Wook Lee (Department of Naval Architecture and Ocean System Engineering, Korea Maritime and Ocean University) ;
  • Sung-Wook Lee (Major of Naval Architecture and Ocean System Engineering, Korea Maritime and Ocean University)
  • 이찬욱 (한국해양대학교 조선해양시스템공학과) ;
  • 이성욱 (한국해양대학교 조선해양시스템공학부)
  • Received : 2023.12.26
  • Accepted : 2024.03.07
  • Published : 2024.04.20
Download PDF
⟨ Previous Next ⟩

Abstract

In the 21st century, the rapid development of automation and artificial intelligence technologies is driving innovative changes in various industrial sectors. In the transportation industry, this is evident with the commercialization of autonomous vehicles. Moreover research into autonomous navigation technologies is actively underway in the aviation and maritime sectors. Consequently, for the practical implementation of autonomous ships, an effective collision avoidance algorithm has become a crucial element. Therefore, this study proposes a collision avoidance algorithm based on the Obstacle Zone by Target(OZT), which visually represents areas with a high likelihood of collisions with other ships or obstacles. The A-star algorithm was utilized to represent obstacles on a grid and assess collision risks. Subsequently, a collision avoidance algorithm was developed that performs fuzzy control based on calculated waypoints, allowing the vessel to return to its original course after avoiding the collision. Finally, the validity of the proposed algorithm was verified through collision avoidance simulations in various encounter scenarios.

Keywords

References

  1. Fiorini, P. and Shiller, Z., 1998. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 17(7), pp.760-772. https://doi.org/10.1177/027836499801700706
  2. Fujii, Y. and Tanaka, K., 1971. Traffic Capacity. The Journal of Navigation, 24(4), pp.543-552. https://doi.org/10.1017/S0373463300022384
  3. Fukuto, J. and Imazu, H., 2013. New collision alarm algorithm using obstacle zone by target(OZT). IFAC Proceedings Volumes, 46, pp.91-96.
  4. Furukawa, Y., Kijima, K. and Ibaragi, H., 2004. Development of automatic course modification system using fuzzy inference. International Federation of Automatic Control Proceedings, 37(10), pp.77-82.
  5. Imazu, H., Fukuto, J. and Numano, M., 2002. Obstacle zone by target and its expression. The Journal of Japan Institute on Navigation, 107, pp.191-197.
  6. Iwasaki, H. and Hara, K., 1986. A fuzzy reasoning model to decide the collision avoidance action. The Journal Japan Institute of Navigation, 75, pp.69-77.
  7. Kijima, K. and Nakiri, Y., 2003. On the practical prediction method for ship manoeuvring characteristics. Transaction of the West-Japan Society of Naval Architects, 105, pp.21-31.
  8. Kim, D.G., Jeong, J.S. and Park, G.K., 2011. A study on ship collision avoidance algorithm by COLREG. Journal of Korean Institute of Intelligent Systems, 21(3), pp.290-295. https://doi.org/10.5391/JKIIS.2011.21.3.290
  9. Kim, D.H., Lee, S.W., Nam, J.H. and Furukawa, Y., 2019. Determination of ship collision avoidance path using deep deterministic policy gradient algorithm. The Society of Naval Architects of Korea, 56(1), pp.58-65.
  10. Kim, D.J. and Kwak, S.Y., 2011. Evaluation of human factors in ship accidents in the domestic sea. Journal of the Ergonomics Society of Korea, 30(1), pp.87-98. https://doi.org/10.5143/JESK.2011.30.1.87
  11. Kim, J.H., Lee, S.W. and Jin, E.S., 2022. Collision avoidance based on predictive probability using Kalman filter. International Journal of Naval Architecture and Ocean Engineering, 14, 100438.
  12. Korean Maritime Safety Tribunal, 2017, Current situation of causes of maritime accidents by type of accident [online] Available at: https://www.kmst.go.kr/kmst/statistics/annualReport/selectAnnual-ReportList.do [Accessed 31 May 2018].
  13. Kuwata, Y., Wolf, M.T., Zarzhitsky, D. and Huntsberger, T.L., 2014. Safe maritime autonomous navigation with COLREGS, using velocity obstacles. IEEE Journal of Ocean Engineering, 39, pp.110-119. https://doi.org/10.1109/JOE.2013.2254214
  14. Mori, S., 1995. Note of ship form design (24). FUNE-NO-KAGAKU, 48, pp.40-49.
  15. Pietrzykowski, Z. and Wielgosz, M., 2021. Effective ship domain-Impact of ship size and speed. Ocean Engineering, 219, 108423.
  16. Sawada, R., Stat, K. and Majima, T., 2020. Automatic ship collision avoidance using deep reinforcement learning with LSTM in continuous action spaces. Journal of Marine Science and Technology, 26(2), pp.509-524. https://doi.org/10.1007/s00773-020-00755-0
  17. Son, D.G. and Kim. D.H., 2021. Autonomous navigation of unmanned surface vehicle based on fuzzy control considering COLREG. Journal of Korean Institute of Intelligent Systems, 31(1), pp.11-20. https://doi.org/10.5391/JKIIS.2021.31.1.011
  18. Son, N.S., Kim, S.Y. and Gong, I.Y., 2009. Study on the estimation of collision risk of ship in ship handling simulator using fuzzy algorithm and environmental stress model. Journal of Navigation and Port Research, 33(1), pp.43-50. https://doi.org/10.5394/KINPR.2009.33.1.043