DEVELOPMENT AND 3D PRINTING OF VESSEL MODELS  WITH AUTOMATED TRAFFIC CONTROL SYSTEMS :

P. Nosov; S. Zinchenko; Ya. Nahrybelnyi; D. Onyshko; V. Polishchuk

P. Nosov Kherson State Maritime Academy, Kherson https://orcid.org/0000-0002-5067-9766
S. Zinchenko Kherson State Maritime Academy, Kherson https://orcid.org/0000-0001-5012-5029
Ya. Nahrybelnyi Kherson State Maritime Academy, Kherson https://orcid.org/0000-0003-3266-5798
D. Onyshko Kherson State Maritime Academy, Kherson https://orcid.org/0009-0005-8394-3091
V. Polishchuk Kherson State Maritime Academy, Kherson https://orcid.org/0000-0003-4970-8010

Keywords: аutonomous navigation systems, human factor, ECDIS/AIS, 3D modeling, remote-control system, PID controllers, maritime navigation

Abstract

The aims to enhance maritime navigation quality and safety by developing autonomous navigation systems that mitigate the negative impact of human factors through advanced software and hardware integration with servers and onboard controllers.

A pivotal challenge faced by researchers is to minimize the potentially negative implications of human factors in the context of vessel management, and to develop efficient mechanisms for software and hardware interaction with servers and onboard controllers.

From a methodological perspective, the research encompasses: a) the development of modules to refine management processes; b) the creation of simulation stands for comprehensive research; and c) the design of a detailed 3D model of the MSC Panaya container ship.

The principal outcomes of our study involve the creation of a detailed 3D model of the MSC Panaya container ship, based on factory blueprints. Utilizing advanced 3D printing technology and PLA plastic, physical models ready for field testing of the proposed technical solutions were successfully fabricated. After selecting and configuring a remote-control system for the ship model, it was ensured to be waterproof, maneuverable, and compatible with other components. Using the chosen remote-control system, the model could operate at a distance of up to 500 meters. Notably, the application of PID controllers assists in stabilizing the vessel under varying weather conditions and marine currents. Furthermore, approaches to optimize hardware components, including microcontrollers, sensors, and associated software, were explored. Emphasizing the development of autopilot systems for ship models up to 2 meters in size, it was discerned that compact sensors such as LIDAR, cameras, and sonars could be particularly beneficial for such vessel models. Additionally, communication systems and integrated GPS modules can simplify navigation and interaction.

The practical contribution of the study is reflected in the development and implementation of comprehensive technical solutions aimed at the optimal interaction of the ship model with water bodies, considering the dynamics of weather conditions and nuances of maritime navigation. Experimental testing under real conditions and modern control systems have augmented the efficiency of the model's operation. Prospects for further research include additional refinement of technical solutions and their adaptation to the needs of real ship systems.

References

1. Nosov, P., Zinchenko, S., Plokhikh, V., Popovych, I., Prokopchuk, Y., Makarchuk, D., Mamenko, P., Moiseienko, V., & Ben, A. (2021). Development and experimental study of analyzer to enhance maritime safety. Eastern-European Journal of Enterprise Technologies, 4/3(112), 27–35. https://doi.org/10.15587/1729-4061.2021.239093.
2. Nosov, P., Koretsky, O., Zinchenko, S., Prokopchuk, Y., Gritsuk, I., Socol, I., Kyrychenko, K. (2023). Devising an approach to safety management of vessel control through the identification of navigator’s state. Eastern-European Journal of Enterprise Technologies, 4(3 (124)), 19–32. doi: https://doi.org/10.15587/1729-4061.2023.286156.
3. Ma, Xiaoxue & Shen, Jun & Liu, Yang & Qiao, Weiliang. (2020). A Methodology to Evaluate the Effectiveness of Intelligent Ship Navigational Information Monitoring System. IEEE Access. 8. https://doi.org/10.1109/ACCESS.2020.3032543.
4. Du, Lei & Valdez Banda, Osiris & Goerlandt, Floris & Huang, Yamin & Kujala, P. (2020). A COLREG-compliant ship collision alert system for stand-on vessels. Ocean Engineering. 218. https://doi.org/10.1016/j.oceaneng.2020.107866.
5. Qiao, Weiliang & Ma, Xiaoxue & Liu, Yang & Lan, He. (2021). Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network. Applied Sciences. 11. https://doi.org/10.3390/app11083619.
6. Qiao, Weiliang & Liu, Yang & Ma, Xiaoxue & Lan, He. (2021). Cognitive Gap and Correlation of Safety-I and Safety-II: A Case of Maritime Shipping Safety Management. Sustainability. 13. 5509. https://doi.org/10.3390/su13105509.
7. Du, Lei & Valdez Banda, Osiris & Huang, Yamin & Goerlandt, Floris & Kujala, P. & Zhang, Weibin. (2021). An empirical ship domain based on evasive maneuver and perceived collision risk. Reliability Engineering System Safety. 213. https://doi.org/ 10.1016/j.ress.2021.107752.
8. Du, Lei & Goerlandt, Floris & Kujala, P. (2020). Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data. Reliability Engineering System Safety. 200. https://doi.org/10.1016/j.ress.2020.106933.
9. Zhang, Mingyang & Conti, Fabien & Sourne, Hervé & Vassalos, Dracos & Kujala, P. & Lindroth, Daniel & Hirdaris, Spyros. (2021). A method for the direct assessment of ship collision damage and flooding risk in real conditions. Ocean Engineering. 237. 20. https://doi.org/10.1016/j.oceaneng.2021.109605.
10. Yu, Qing & Teixeira, A.P. & Liu, Kezhong & Rong, Hao & Soares, Carlos. (2021). An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning. Reliability Engineering & System Safety. 216. 107993. https://doi.org/10.1016/j.ress.2021.107993.
11. Zhang, Ting & Yang, Jing & Liang, Nade & Pitts, Brandon & Prakah-Asante, Kwaku & Curry, Reates & Duerstock, Bradley & Wachs, Juan & Yu, Denny. (2020). Physiological Measurements of Situation Awareness: A Systematic Review. Human Factors: The Journal of the Human Factors and Ergonomics Society. 001872082096907. https://doi.org/10.1177 /0018720820969071.
12. Elidolu, Gizem & Uyanık, Tayfun & Arslanoğlu, Yasin. (2020). Seafarer personnel selection with Fuzzy AHP / 5th International Mediterranean Science and Engineering Congress (IMSEC 2020) At: Antalya/Turkey.
13. Saeed, Farhan & Bury, Alan & Bonsall, Stephen & Riahi, Ramin. (2019). The application of AHP in the development of a taxonomy of merchant marine deck officers’ non-technical skills (NTS). Logistics & Sustainable Transport. 10. 55–70. https://doi.org/10.2478/jlst-2019-0005.
14. Zinchenko, S., Kobets, V., Tovstokoryi, O., Nosov, P., & Popovych, I. (2023). Intelligent System Control of the Vessel Executive Devices Redundant Structure. In CEUR Workshop Proceedings (Vol. 3403, Paper 44, pp. 582–594). CEUR-WS.org.
15. Zinchenko, S., Tovstokoryi, O., Mateichuk, V., Nosov, P., Popovych, I., & Gritsuk, I. (2022). Automatic Vessel Steering in a Storm. ECCE, 18(1), 66–74. DOI: 10.2478/ecce-2022-0009.
16. Bošnjak, Rino & Bukljaš, Mihaela & Medic, Dario & Vukša, Srđan. (2021). Scientific Journals Zeszyty Naukowe of the Maritime University of Szczecin Akademii Morskiej w Szczecinie Proposal of new measures for risk assessment in navigation -a case study of the M/V Cosco Busan accident. Zeszyty Naukowe Akademii Marynarki Wojennej. 67. 2021.
17. Zhou, Yangyu & Yang, Jiaxuan & Liu, Yuan & Liu, Jiaguo. (2020). Research on Time Characteristics of Near Miss in Bohai Sea. IEEE Access. 8. 207717-207735. https://doi.org/10.1109/ACCESS.2020.3038795.
18. Cepeda F. S., Maricruz & Monteiro, Gabriel & Moita, João Vitor & Caprace, Jean. (2018). Estimating possible near miss collisions based on AIS big data for the Port of Rio de Janeiro. https://doi.org/10.17648/sobena-2018-87574.
19. Altun, Murat & Kum, Serdar. (2019). Safety Indications of Navigation Audit for Tankers to Develop a Bridge Inspection Method. Journal of Computer and Communications. 07. 18–33. https://doi.org/10.4236/jcc.2019.71003.
20. Tarelko, Wieslaw & Rudzki, Krzysztof. (2020). Applying artificial neural networks for modelling ship speed and fuel consumption. Neural Computing and Applications. 32. https://doi.org/10.1007/s00521-020-05111-2.
21. TS2 Space. (n.d.). Retrieved from https://ts2.space.
22. FlySky. (n.d.). i6x Overview. Retrieved from https://www.flysky-cn.com/i6x-gaishu-1.
23. FlySky. (2016). FS-i6X User manual. Retrieved from https://static1. squarespace.com/static/5bc852d6b9144934c40d499c/t/5d6f66305487dc0001e34939/1567581800555/FS-i6X+User+manual+20160817.pdf.
24. Dronomania. (n.d.). Drone with your hands Lesson 4: Flight controller. Retrieved from https://dronomania.ru/faq/dron-svoimi-rukami-urok-4-polyotnyj-kontroller.html.
25. RC-Boat. (n.d.). [Product information]. Retrieved from https://rc-boat. ozp.biz/product_48.html.

DEVELOPMENT AND 3D PRINTING OF VESSEL MODELS WITH AUTOMATED TRAFFIC CONTROL SYSTEMS

https://doi.org/10.33815/2313-4763.2023.1-2.26-27.070-081

Abstract

References