Computational intelligence in marine control engineering education

• Autorzy: Lisowski Józef

Identyfikatory

DOI

10.2478/pomr-2021-0015

• Języki publikacji: EN

Abstrakty

EN

This paper presents a new approach to the existing training of marine control engineering professionals using artificial intelligence. We use optimisation strategies, neural networks and game theory to support optimal, safe ship control by applying the latest scientific achievements to the current process of educating students as future marine officers. Recent advancements in shipbuilding, equipment for robotised ships, the high quality of shipboard game plans, the cost of overhauling, dependability, the fixing of the shipboard equipment and the requesting of the safe shipping and environmental protection, requires constant information on recent equipment and programming for computational intelligence by marine officers. We carry out an analysis to determine which methods of artificial intelligence can allow us to eliminate human subjectivity and uncertainty from real navigational situations involving manoeuvring decisions made by marine officers. Trainees learn by using computer simulation methods to calculate the optimal safe traverse of the ship in the event of a possible collision with other ships, which are mapped using neural networks that take into consideration the subjectivity of the navigator. The game-optimal safe trajectory for the ship also considers the uncertainty in the navigational situation, which is measured in terms of the risk of collision. The use of artificial intelligence methods in the final stage of training on ship automation can improve the practical education of marine officers and allow for safer and more effective ship operation.

Słowa kluczowe

EN

ship control; marine engineering curriculum; computational intelligence; game theory; computer simulation

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2021
• Tom: nr 1
• Strony: 163--172
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

Lisowski Józef

• Uniwersytet Morski w Gdyni, Morska 83, 81-225 Gdynia, Poland

Bibliografia

• 1. J.H. Ahn, K.P. Rhee, and Y.J You,“A study on the collision avoidance of a ship using neural networks and fuzzy logic,” Applied Ocean Research, vol. 37, pp. 162–173, 2012. DOI: 10.1016/j.apor.2012.05.008
• 2. R.E. Bellman, Dynamic Programming. New York: Dover Publications, 2003. ISBN 0-486-42809-5
• 3. M. Borrego, E.P. Douglas, and C.T. Amelink, Quantitative, “Qualitative and mixed research methods in engineering education,” Journal of Engineering Education, vol. 98, no. 1, pp. 53–66, 2009. DOI: 10.1002/j.2168-9830.2009.tb01005.x
• 4. R. Cwilewicz and J. Lisowski, “The integrated maritime education and research activity of Gdynia Maritime University,” in 12th Annual General Assembly of IAMU - Green Ships, Eco Shipping, Clean Seas, Gdynia Maritime University, Gdynia, 17 June 2011, pp. 87–98.
• 5. B. Guenin, J. Konemann, and L.A. Tuncel, Gentle Introduction to Optimization. Cambridge, UK: Cambridge University Press, 2014. ISBN 978-1-107-05344-1
• 6. S.S. Guzey and M. Aranda, “Student participation in engineering practices and discourse: An exploratory case study,” Journal of Engineering Education, vol. 106, no. 4, pp. 585–606, 2017. DOI: 10.1002/jee.20176
• 7. H. Heiselberg and A. Stateczny, “Remote sensing in vessel detection and navigation,” Sensors, vol. 20, pp. 1–9, 2020. DOI: 10.3390/s20205841
• 8. M. Henri, M.D. Johnson, and B. Nepal, “A review of competency-based learning: Tools, assessments, and recommendations,” Journal of Engineering Education, vol. 106, no. 4, pp. 607–638, 2017. DOI: 10.1002/jeee.20180
• 9. L. Hongguang and Y. Yong, “COLREGS-constrained realtime path planning for autonomous ships using modified artificial potential fields,” Journal of Navigation, vol. 71, pp. 1–21, 2018. DOI: 10.1017/S0373463318000796
• 10. Y. Huang, L. Chen, P. Chen, R.R. Negenborn, and P.H.A.J.M. van Gelder, “Ship collision avoidance methods: State-ofthe-art,” Safety Science, vol. 121, pp. 451–473, 2020. DOI: 10.1016/j.ssci.2019.09.018
• 11. K.S. Kula, “Automatic control of ship motion conducting search in open waters,” Polish Maritime Research, vol. 27, no. 4, pp. 157-169, 2020. DOI: 10.2478/pomr-2020-0076
• 12. L.R. Lattuca, D.B. Knight, H.K. Ro, and B.J. Novoselich, “Supporting the development of engineers’ interdisciplinary competence,” Journal of Engineering Education, vol. 106, no. 1, pp. 71–97, 2017. DOI: 10.1002/jeee.20155
• 13. A. Lazarowska, “Comparison of discrete artificial potential field algorithm and wave-front algorithm for autonomous ship trajectory planning,” IEEE Access, vol. 8, pp. 221013– 221026, 2020. DOI: 10.1109/ACCESS.2020.3043539
• 14. A. Lebkowski, “Evolutionary methods in the management of vessel traffic,” in Proc. Int. Conf. on Marine Navigation and Safety of Sea Transportation, Gdynia, Poland, 17 June 2015, pp. 259–266. DOI: 10.12716/1001.12.01.13
• 15. J. Lisowski, “Multi-criteria optimization of multi-stage positional game of vessels,” Polish Maritime Research, vol. 27, no. 1, pp. 46-52, 2020. DOI: 10.2478/pomr-2020-0005
• 16. Z. Liu, Z. Wu, and Z. Zheng, “A cooperative game approach for assessing the collision risk in multi-vessel encountering,” Ocean Engineering, vol. 187, pp. 1–12, 2019. DOI: 10.1016/j. oceaneng.2019.106175
• 17. Z. Liu, “Pre-filtered backstepping control for underactuated ship path following,” Polish Maritime Research, vol. 26, no. 2, pp. 68-75, 2019. DOI: 10.2478/pomr-2019-0026
• 18. S. Nikolic, “Improving the laboratory learning experience: A process to train and manage teaching assistants,” IEEE Transaction on Education, vol. 58, no. 2, pp.130–139, 2015. DOI: 10.1109/TE.2014.2335712
• 19. N.S. Nise, Control Systems Engineering. New York: John Wiley & Sons, 2019. ISBN 978-1-119-72140-6
• 20. M.J. Osborne, An Introduction to Game Theory. New York: Oxford University Press, 2004.
• 21. P.V. Reddy and G. Zaccour, “Feedback Nash equilibria in linear-quadratic difference games with constraints,” IEEE Transactions on Automatic Control, vol. 62, pp. 590–604, 2016. DOI: 10.1109/TAC.2016.2555879
• 22. J. Sanchez-Soriano, “An overview of game theory applications to engineering,” International Game Theory Review, vol. 15, pp. 1–18, 2013. DOI: 10.1142/ S0219198913400197
• 23. L. Song, H. Chen, W. Xiong, et al., “Method of emergency collision avoidance for unmanned surface vehicle (USV) based on motion ability database,” Polish Maritime Research, vol. 26, no. 2, pp. 55-67, 2019. DOI: 10.2478/ pomr-2019-0025
• 24. J.L. Speyer and D.H. Jacobson, Primer on Optimal Control Theory. Toronto, Canada: SIAM, 2010. ISBN 978-0-898716-94-8
• 25. J. Szlapczynska and R. Szlapczynski, “Preference-based evolutionary multi-objective optimization in ship weather routing,” Applied Soft Computing, vol. 84, pp. 1–21, 2019. DOI: 10.1016/j.asoc.2019.105742
• 26. S. Wang, Y. Tuo, “Robust trajectory tracking control of underactuated surface vehicles with prescribed performance,” Polish Maritime Research, vol. 27, no. 4, pp. 148-156, 2020. DOI: 10.2478/pomr-2020-0075
• 27. J. Trevelyan, “Technical coordination in engineering practice,” Journal of Engineering Education, vol. 96, no. 3, pp. 191–204, 2007. DOI: 10.1002/j.2168-9830.2007.tb00929.x
• 28. H.J. Trussell and E.J. Dietz, “A study of the effect of graded homework in a preparatory math course for electrical engineers,” Journal of Engineering Education, vol. 92, no. 2, pp. 141–146, 2003. DOI: 10.1002/j.2168-9830.2003. tb00752.x
• 29. T.F. Weisner and W. Lan, “Comparison of student learning in physical and simulated unit operations experiments,” Journal of Engineering Education, vol. 3, no. 3, pp. 5–12, 2004. DOI: 10.1002/2168-9830.2004.tb00806.x
• 30. A. Witkowska and R. Smierzchalski, “Adaptive dynamic control allocation for dynamic positioning of marine vessel based on backstepping method and sequential quadratic programming,” Ocean Engineering, vol. 163, pp. 570–582, 2018. DOI: 10.1016/j.oceaneng.2018.05.061
• 31. J. Yong, Optimization Theory – A Concise Introduction. New Jersey: World Scientific, 2018. ISBN 978-981-3237-64-3
• 32. J. Zhuang, L. Zhang, Z. Qin, et al., “Motion control and collision avoidance algorithms for unmanned surface vehicle swarm in practical maritime environment,” Polish Maritime Research, vol. 26, no. 1, pp. 107-116, 2019. DOI: 10.2478/pomr-2019-0012

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).