Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Advances in Artificial Systems for Medicine and Education III (AIMEE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1126))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tsou, M.C.: Multi-target collision avoidance route planning under an ECDIS framework. Ocean Eng. 121, 268–278 (2016). https://doi.org/10.1016/j.oceaneng.2016.05.040

    Article  Google Scholar 

  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). https://doi.org/10.1109/ICICTA.2015.69

  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. Lisowski, J.: Computational intelligence methods of a safe ship control. Procedia Comput. Sci. 35, 634–643 (2014). https://doi.org/10.1016/j.procs.2014.08.145

    Article  Google Scholar 

  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). https://doi.org/10.1243/14750902jeme123

    Article  Google Scholar 

  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). https://doi.org/10.1155/2018/2367096

    Article  Google Scholar 

  7. Emmanuel, I.: Fuzzy logic-based control for autonomous vehicle: a survey. Int. J. Educ. Manag. Eng. (IJEME) 7(2), 41–49 (2017). https://doi.org/10.5815/ijeme.2017.02.05

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijisa.2014.12.01

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijisa.2018.02.08

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijitcs.2015.02.08

    Article  Google Scholar 

  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). https://doi.org/10.1109/INFOCOMMST.2015.7357330

  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). https://doi.org/10.1109/CIS.2008.211

  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). https://doi.org/10.1109/ICCAR.2015.7166009

  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). https://doi.org/10.1109/3468.784177

    Article  Google Scholar 

  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). https://doi.org/10.1109/FSKD.2015.7381966

  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). https://doi.org/10.1109/FUZZY.1992.258782

  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). https://doi.org/10.1109/cso.2012.88

  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). https://doi.org/10.1109/APEIE.2016.7806454

Download references

Author information

Authors and Affiliations

  1. Maritime State University named after G.I. Nevelskoy, Vladivostok, Russian Federation

    Nelly Sedova & Viktor Sedov

  2. Sholom-Aleichem Priamursky State University, Birobidzhan, Russian Federation

    Ruslan Bazhenov

Authors
  1. Nelly Sedova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Viktor Sedov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruslan Bazhenov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruslan Bazhenov .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute, Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Davie, FL, USA

    Matthew He

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sedova, N., Sedov, V., Bazhenov, R. (2020). Preventing Ship Collision with Stationary Sea Crafts Through a Fuzzy Logic Method. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education III. AIMEE 2019. Advances in Intelligent Systems and Computing, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-030-39162-1_44

Download citation

Publish with us

Policies and ethics

Search

Navigation