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A collision avoidance method for multi-ship encounter situations

  • Man-Chun Lee
  • Chung-Yuan Nieh
  • Hsin-Chuan Kuo
  • Juan-Chen HuangEmail author
Original article
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Abstract

This study proposes a multi-ship collision avoidance and route generating algorithm based on the general requirements of the International Regulations for Preventing Collisions at Sea (COLREGs) and the artificial potential field (APF) method. The velocity potential field is used as the field function of APF method. The algorithm consists of two modes, the course-changing and the track-keeping modes based on the velocity potential of vortex and dipole, respectively. The course-changing mode guides the ship to turn away from the obstacles according to the vector field of vortex potential. The track-keeping mode steers the ship back to and along on a pre-designed track in accordance with the vector field of dipole potential. The data of distance to the closest point of approach (DCPA), time to the closest point of approach (TCPA) and bearing angle evaluated from a maneuvering simulation are the acquired parameters of the proposed collision avoidance algorithm. The algorithm is straightforward and very simple to implement, and is suitable for the real time and distributed intelligent collision avoidance system. Simulation results indicate that the anti-collision formulation can avoid collision safely with the desired distance and indicate that the algorithm proposed can work effectively.

Keywords

Automatic collision avoidance Route generating Course-keeping Track-keeping Velocity potential field Intelligent navigation COLREGs 

Notes

Acknowledgements

The research is supported by the Ministry of Science and Technology, Taiwan (Grant No: MOST 103-2410-H-019-008-MY2).

References

  1. 1.
    Fossen TI (1999) Guidance and control of ocean vehicles. Wiley, New YorkGoogle Scholar
  2. 2.
    Ito M, Zhang FF, Yoshida N (1999) Collision avoidance control of ship with genetic algorithms. In: Proceedings of the 1999 IEEE international conference on control applications, Kohala Coast, vol 2, pp 1791–1796Google Scholar
  3. 3.
    Smierzchalski R (1999) Evolutionary trajectory planning of ships in navigation traffic areas. J Mar Sci Technol 4(1):1–6CrossRefGoogle Scholar
  4. 4.
    Smierzchalski R, Michalewicz Z (2000) Modeling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Trans Evolut Comput 4(3):227–241CrossRefGoogle Scholar
  5. 5.
    Tsou MC, Hsueh CK (2010) The study of ship collision avoidance route planning by antcolony algorithm. J Mar Sci Technol 18(5):746–756Google Scholar
  6. 6.
    Szlapczynski R, Szlapczynska J (2012) Customized crossover in evolutionary sets of safe ship trajectories. Int J Appl Math Comput Sci 22(4):999–1009zbMATHCrossRefGoogle Scholar
  7. 7.
    Tam C, Bucknall R (2010) Path-planning algorithm for ships in close-range encounters. J Mar Sci Technol 15(4):395–407CrossRefGoogle Scholar
  8. 8.
    Tam C, Bucknall R (2013) Cooperative path planning algorithm for marine surface vessels. Ocean Eng 57:25–33CrossRefGoogle Scholar
  9. 9.
    Tsou MC, Kao SL, Su CM (2010) Decision support from genetic algorithms for ship collision avoidance route planning and alerts. J Navig 63(01):167–182CrossRefGoogle Scholar
  10. 10.
    The International Maritime Organization (IMO) (1972) Conventions on the International Regulations for Preventing Collision at Sea (COLREGs), London, U.KGoogle Scholar
  11. 11.
    Perera LP, Carvalho JP, Guedes Soares C (2011) Fuzzy-logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. J Mar Sci Technol 16(1):84–99CrossRefGoogle Scholar
  12. 12.
    Lee SM, Kwon KY, Joh J (2004) A fuzzy autonomous navigation of marine vehicles satisfying COLREGS guidelines. Control Autom 2(2):171–181Google Scholar
  13. 13.
    Benjamin MR, Leonard JJ, Curcio JA, Newman PM (2006a) A method for protocol-based collision avoidance between autonomous marine surface craft. J Field Robot 23(5):333–346CrossRefGoogle Scholar
  14. 14.
    Benjamin MR, Curcio JA, Leonard JJ, Newman PM (2006) Navigation of unmanned marine vehicles in accordance with the rules of the road. In: Proceedings of the IEEE international conference on robotics and automation, pp 3581–3587Google Scholar
  15. 15.
    Perera LP, Carvalho JP, Guedes Soares C (2012) Intelligent ocean navigation and fuzzy-Bayesian decision/action formulation. IEEE J Ocean Eng 37(2):204–219CrossRefGoogle Scholar
  16. 16.
    Perera LP, Carvalho JP, Guedes Soares C (2014) Solutions to the failure and limitations of mamdani fuzzy inference in ship navigation. IEEE Trans Veh Technol 63(4):1539–1554CrossRefGoogle Scholar
  17. 17.
    He Y, Jin Y, Huang L, Xiong Y, Chen P, Mou J (2017) Quantitative analysis of COLREG rules and seamanship for autonomous collision avoidance at open sea. Ocean Eng 140:281–291CrossRefGoogle Scholar
  18. 18.
    Szlapczynski R (2011) Evolutionary sets of safe ship trajectories: a new approach to collision avoidance. J Navig 64(1):169–181CrossRefGoogle Scholar
  19. 19.
    Szlapczynski R (2015) Evolutionary planning of safe ship tracks in restricted visibility. J Navig 68(1):39–51CrossRefGoogle Scholar
  20. 20.
    Kim D, Hirayama K, Park G (2014) Collision avoidance in multiple-ship situations by distributed local search. J Adv Comput Intell Intell Inform 18(5):839–848CrossRefGoogle Scholar
  21. 21.
    Kim D, Hirayama K, Okimoto T (2015) Ship collision avoidance by distributed Tabu search. Int J Mar Navig Saf Sea Transp 9(1):23–29CrossRefGoogle Scholar
  22. 22.
    Kim D, Hirayama K, Okimoto T (2017) Distributed stochastic search algorithm for multi-ship encounter situations. J Navig 70:699–718CrossRefGoogle Scholar
  23. 23.
    Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Robot Res 5(1):90–98CrossRefGoogle Scholar
  24. 24.
    Noto N, Okuda H, Tazaki Y, Suzuki T (2012) Steering assisting system for obstacle avoidance based on personalized potential field. In: IEEE International Conference on Intelligent Transportation Systems, pp 1702–1707Google Scholar
  25. 25.
    Kovács B, Szayer G et al (2016) A novel potential field method for path planning of mobile robots by adapting animal motion attributes. Robot Auton Syst 82(C):24–34CrossRefGoogle Scholar
  26. 26.
    Macktoobian M, Shoorehdeli MA (2016) Time-variant artificial potential field (TAPF): a breakthrough in power-optimized motion planning of autonomous space mobile robots. Robotica 35(5):1–23Google Scholar
  27. 27.
    Xue Y, Clelland D, Lee BS, Han DF (2011) Automatic simulation of ship navigation. Ocean Eng 38(17–18):2290–2305CrossRefGoogle Scholar
  28. 28.
    Xiao FL, Ligteringen H, van Gulijk C, Ale B (2012) Artificial force fields for multi-agent simulations of maritime traffic: a case study of Chinese waterway. Proc Eng 45:807–814CrossRefGoogle Scholar
  29. 29.
    Rong H, Teixeira A, Soares CG (2015) Evaluation of near-collisions in the Tagus River Estuary using a marine traffic simulation model. Sci J Marit Univ Szczec 43(115):68–78Google Scholar
  30. 30.
    Naeem W, Henrique SC, Hu L (2016) A reactive COLREGs—compliant navigation strategy for autonomous maritime navigation. IFAC-PapersOnLine 49(23):207–213CrossRefGoogle Scholar
  31. 31.
    Wang TF, Yan XP, Wang Y (2017) Ship domain model for multi-ship collision avoidance decision-making with COLREGs based on artificial potential field. TransNav Int J Mar Navig Saf Sea Transp 11(1):85–92CrossRefGoogle Scholar
  32. 32.
    Zhang J, Zhang D, Yan X, Haugen S, Soares CG (2015) A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs. Ocean Eng 105:336–348CrossRefGoogle Scholar
  33. 33.
    Shibata N, Sugiyama S, Wada T (2014) Collision avoidance control with steering using velocity potential field. In: IEEE intelligent vehicles symposium (IV), June 8–11, 2014, Dearborn, pp 438–443Google Scholar
  34. 34.
    Burgos E, Bhandari S (2016) Potential flow field navigation with virtual force field for UAS collision avoidance. In: International conference on unmanned aircraft systems (ICUAS), June 7–10, 2016, ArlingtonGoogle Scholar
  35. 35.
    Hu XP, Li ZY, Cao J (2017) A path planning method based on artificial potential field improved by potential flow theory. In: 2017 2nd international conference on computer science and technology (CST 2017), pp 617–625 (ISBN: 978-1-60595-461-52016) Google Scholar
  36. 36.
    Kijima K (1991) Prediction method for ship manoeuvring performance in deep and shallow waters. In: Presented at the workshop on modular manoeuvring models, The Society of Naval Architects and Marine EngineeringGoogle Scholar
  37. 37.
    Yavin Y, Frangos C, Miloh T (1995) Computation of feasible control trajectories for the navigation of a ship around an obstacle in the presence of a sea current. Math Comput Model 21(3):99–117MathSciNetzbMATHCrossRefGoogle Scholar
  38. 38.
    Belenky V, Falzarano J (2006) Rating-based maneuverability standards. ABS Technical Papers 2006. Originally presented at the SNAME 2006 Annual Meeting Conference held in Ft. Lauderdale, Florida, October 10–13, pp 227–246Google Scholar

Copyright information

© The Japan Society of Naval Architects and Ocean Engineers (JASNAOE) 2019

Authors and Affiliations

  1. 1.Department of Merchant MarineNational Taiwan Ocean UniversityKeelungTaiwan
  2. 2.Maritime Development and Training CenterNational Taiwan Ocean UniversityKeelungTaiwan
  3. 3.Department of Systems Engineering and Naval ArchitectureNational Taiwan Ocean UniversityKeelungTaiwan

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