Preventing Ship Collision with Stationary Sea Crafts Through a Fuzzy Logic Method

  • Nelly Sedova
  • Viktor Sedov
  • Ruslan BazhenovEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)


The paper suggests an approach based on fuzzy sets applied to the problem concerning the rules of the road between vessels and fixed sea crafts. The authors develop and test a fuzzy model, which is informed on foreign objects that need to avoid, the distance between them and their position relating to one’s own vessel from the shipboard radar. The model gives recommendations to a skipper for the maneuver of the safe passing in case when it is necessary. The authors define three input linguistic variables for the production model. They are the positions on the left, in front, and on the right. What is more, there is one output linguistic variable—bearing. 25 terms of four basic term sets are defined. They also set membership function parameters for each term. Fuzzy inference is based on Mamdani model and contains a fuzzy production rule base consisting of 216 rules. Testing the model shows good performance when determining the deviation from set course if there is a safe passing such obstacles as fixed sea crafts is required. The survey describes the efficiency of the model by example of three characteristic test cases.


COLREG Vessel traffic Collision avoidance actions Fuzzy logic system 


  1. 1.
    Tsou, M.C.: Multi-target collision avoidance route planning under an ECDIS framework. Ocean Eng. 121, 268–278 (2016). Scholar
  2. 2.
    He, J.C.: Based on ECDIS and AIS ship collision avoidance warning system research. In: 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 242–245. IEEE Press, Nanchang (2015).
  3. 3.
    Su, C.M., Chang, K.Y., Cheng, C.Y.: Fuzzy decision on optimal collision avoidance measures for ships in vessel traffic service. J. Mar. Sci. Technol. 20(1), 38–48 (2012)Google Scholar
  4. 4.
    Lisowski, J.: Computational intelligence methods of a safe ship control. Procedia Comput. Sci. 35, 634–643 (2014). Scholar
  5. 5.
    Xue, Y., Lee, B.S., Han, D.: Automatic collision avoidance of ships. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 223, 33–46 (2015). Scholar
  6. 6.
    Sedova, N.A., Sedov, V.A., Bazhenov, R.I.: The neural-fuzzy approach as a way of preventing a maritime vessel accident in a heavy traffic zone. Adv. Fuzzy Syst. 2018, 2367096 (2018). Scholar
  7. 7.
    Emmanuel, I.: Fuzzy logic-based control for autonomous vehicle: a survey. Int. J. Educ. Manag. Eng. (IJEME) 7(2), 41–49 (2017). Scholar
  8. 8.
    Laouici, Z., Mami, M.A., Khelfi, M.F.: Hybrid method for the navigation of mobile robot using fuzzy logic and spiking neural networks. Int. J. Intell. Syst. Appl. (IJISA) 6(12), 1–9 (2014). Scholar
  9. 9.
    Boufera, F., Debbat, F., Monmarché, N., Slimane, M., Khelfi, M.F.: Fuzzy inference system optimization by evolutionary approach for mobile robot navigation. Int. J. Intell. Syst. Appl. (IJISA) 10(2), 85–93 (2018). Scholar
  10. 10.
    Heidari, S., Shahcheraghi, A., Heidari, K., Zahmatkesh, S., Piltan, F.: Intelligent adaptive gain backstepping technique. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 7(2), 60–67 (2015). Scholar
  11. 11.
    Korchenko, O., Kazmirchuk, S., Akhmetov, B., Zhekambaeva, M.: Increment order of linguistic variables method in information security risk assessment. In: 2015 Second International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T), pp. 259–262. IEEE Press, Kharkiv (2015).
  12. 12.
    Derbel, I., Hachani, N., Ounelli, H.: Membership functions generation based on density function. In: 2008 International Conference on Computational Intelligence and Security, pp. 96–101. IEEE Press, Suzhou (2008).
  13. 13.
    Sebastião, A., Lucena, C., Palma, L., Cardoso, A., Gil, P.: Optimal tuning of scaling factors and membership functions for Mamdani type PID fuzzy controllers. In: 2015 International Conference on Control, Automation and Robotics, pp. 92–96. IEEE Press, Singapore (2015).
  14. 14.
    Ying, H., Ding, Y., Li, S., Shao, S.: Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 29(5), 508–514 (1999). Scholar
  15. 15.
    Chang, W., Hsu, F.L.: Mamdani and Takagi-Sugeno fuzzy controller design for ship fin stabilizing systems. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 345–350. IEEE Press, Zhangjiajie (2015).
  16. 16.
    Bugariin, A., Barro, S., Ruiz, R.: Compacting rules for fuzzy production system computation. In: 1992 Proceedings IEEE International Conference on Fuzzy Systems, pp. 933–940. IEEE Press, San Diego (1992).
  17. 17.
    Pu, Y., Wang, W., Xu, Q.: Image change detection based on the minimum mean square error. In: 2012 Fifth International Joint Conference on Computational Sciences and Optimization, pp. 367–371. IEEE Press, Harbin (2012).
  18. 18.
    Veretelnikova, E.L., Elantseva, I.L.: Selection of factor for root mean square minimum error criterion. In: 2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE), pp. 221–223. IEEE Press, Novosibirsk (2016).

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Maritime State University named after G.I. NevelskoyVladivostokRussian Federation
  2. 2.Sholom-Aleichem Priamursky State UniversityBirobidzhanRussian Federation

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