Skip to main content
Springer Nature Link
Log in

Impact of the Grid Size on the Performance of Ant Colony Optimization-Based Algorithm for Ship Safe Path Planning

  • Conference paper
  • First Online:
Advanced, Contemporary Control (PCC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 709))

Included in the following conference series:

  • 251 Accesses

Abstract

The paper presents results of a research on the development of ship safe path planning algorithms for the application in the Decision Support System on-board a manned vessel or in the Autonomous Navigation System of unmanned or fully autonomous ships. The method applied for solving this problem is based on the Ant Colony Optimization, belonging to the Swarm Intelligence approaches. The main purpose of the presented study was to evaluate the influence of the grid size on obtained solutions and the run time of the algorithm. The ship safe path planning problem is firstly defined, followed by the description of the developed Ant Colony Optimization-based algorithm. Results of an exemplary test case, along with some conclusions formulated based on them, are also presented.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Blum, Ch., Vallès, M.Y., Blesa, M.J.: An ant colony optimization algorithm for DNA sequencing by hybridization. Comput. Oper. Res. 35(11), 3620–3635 (2008). https://doi.org/10.1016/j.cor.2007.03.007

    Article  MATH  Google Scholar 

  2. Chen, X., Kong, Y., Fang, X., et al.: A fast two-stage ACO algorithm for robotic path planning. Neural Comput. Appl. 22, 313–319 (2013). https://doi.org/10.1007/s00521-011-0682-7

    Article  Google Scholar 

  3. Xin-She, Y., Cui, Z., Xiao, R., et al.: Swarm intelligence and bio-inspired computation: theory and applications. Elsevier Ltd. (2013). https://doi.org/10.1016/C2012-0-02754-8

    Article  Google Scholar 

  4. Yang, X.-S., Zhao, Y.-X. (eds.): Nature-Inspired Computation in Navigation and Routing Problems. STNC, Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-1842-3

    Book  Google Scholar 

  5. Wei, G., Kuo, W.: COLREGs-compliant multi-ship collision avoidance based on multi-agent reinforcement learning technique. J. Marine Sci. Eng. 10(10), 1431 (2022). https://doi.org/10.3390/jmse10101431

    Article  Google Scholar 

  6. Jiang, L., An, L., Zhang, X., et al.: A human-like collision avoidance method for autonomous ship with attention-based deep reinforcement learning. Ocean Eng. 264, 112378 (2022). https://doi.org/10.1016/j.oceaneng.2022.112378

    Article  Google Scholar 

  7. Zhu, Z., Lyu, H., Zhang, J., et al.: An efficient ship automatic collision avoidance method based on modified artificial potential field. J. Marine Sci. Eng. 10(1), 3 (2022). https://doi.org/10.3390/jmse10010003

    Article  Google Scholar 

  8. Cho, Y., Han, J., Kim, J.: Efficient COLREG-Compliant Collision Avoidance in Multi-Ship Encounter Situations. IEEE Trans. Intell. Transp. Syst. 23(3), 1899–1911 (2022). https://doi.org/10.1109/TITS.2020.3029279

    Article  Google Scholar 

  9. Lisowski, J.: Review of ship collision avoidance guidance algorithms using remote sensing and game control. Remote Sensing 14, 4928 (2022). https://doi.org/10.3390/rs14194928

    Article  Google Scholar 

  10. Mohamed-Seghir, M., Kula, K., Kouzou, A.: Artificial intelligence-based methods for decision support to avoid collisions at sea. Electronics 10(19), 2360 (2021). https://doi.org/10.3390/electronics10192360

    Article  Google Scholar 

  11. Lisowski, J.: Artificial intelligence methods in safe ship control based on marine environment remote sensing. Remote Sensing 15, 203 (2023). https://doi.org/10.3390/rs15010203

    Article  Google Scholar 

  12. Szłapczyński, R., Ghaemi, H.: Framework of an evolutionary multi-objective optimisation method for planning a safe trajectory for a marine autonomous surface ship. Polish Maritime Research 26(4), 69–79 (2019). https://doi.org/10.2478/pomr-2019-0068

    Article  Google Scholar 

  13. Witkowska, A., Rynkiewicz, T.: Dynamically positioned ship steering making use of backstepping method and artificial neural networks. Polish Maritime Res. 25(4), 5–12 (2018). https://doi.org/10.2478/pomr-2018-0126

    Article  Google Scholar 

  14. Tomera, M.: Path controller for ships with switching approach. In: Bartoszewicz, A., Kabziński, J., Kacprzyk, J. (eds.) Advanced, Contemporary Control. AISC, vol. 1196, pp. 1519–1530. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50936-1_126

    Chapter  Google Scholar 

  15. Rybczak, M., Podgórski, K.: Pareto effect of LMI for ship propulsion. Appl. Sci. 11(16), 7297 (2021). https://doi.org/10.3390/app11167297

    Article  Google Scholar 

  16. Esfahani, H.N., Szlapczynski, R.: Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm. J. Mar. Sci. Technol. 26(4), 1262–1279 (2021). https://doi.org/10.1007/s00773-021-00813-1

    Article  Google Scholar 

  17. Tomera, M.: Ant colony optimization algorithm applied to ship steering control. Procedia Comput. Sci. 35, 83–92 (2014). https://doi.org/10.1016/j.procs.2014.08.087

    Article  Google Scholar 

  18. Tomera, M.: Swarm intelligence applied to identification of nonlinear ship steering model. In: Proceedings of the IEEE 2nd International Conference on Cybernetics (CYBCONF), pp. 133–139 (2015). https://doi.org/10.1109/CYBConf.2015.7175920

  19. Cockcroft, A.N., Lameijer J.N.F.: A Guide to the Collision Avoidance Rules. Elsevier Ltd (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Ship Automation, Gdynia Maritime University, 81-87 Morska Street, 81-225, Gdynia, Poland

    Agnieszka Lazarowska

Authors
  1. Agnieszka Lazarowska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Agnieszka Lazarowska .

Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Marek Pawelczyk

  2. Silesian University of Technology, Gliwice, Poland

    Dariusz Bismor

  3. Silesian University of Technology, Gliwice, Poland

    Szymon Ogonowski

  4. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Lazarowska, A. (2023). Impact of the Grid Size on the Performance of Ant Colony Optimization-Based Algorithm for Ship Safe Path Planning. In: Pawelczyk, M., Bismor, D., Ogonowski, S., Kacprzyk, J. (eds) Advanced, Contemporary Control. PCC 2023. Lecture Notes in Networks and Systems, vol 709. Springer, Cham. https://doi.org/10.1007/978-3-031-35173-0_35

Download citation

Publish with us

Policies and ethics