A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions

Abstract

A mathematical model is created to obtain safe navigation for ships in regular head waves in this study. To validate the suggested model, firstly, the added resistances are calculated for two different ships using empirical formulas in the mathematical model. Secondly, the turning test simulations are performed for calm water and in waves with various wave amplitudes. After these validation studies, the path following simulation of the ship to the target destinations is performed in both waves and calm water for the determined course. It is assumed that regular head waves affect the ship as an external disturbance. The wavelengths and wave amplitudes are changed systematically to understand their effect during the path following simulations. When the ratio of wavelength to ship length, λ/Lpp, is nearly 1.0, the path following simulation times increase. Moreover, when the value of wave amplitude increases, so does the simulation time.

Keywords

• Ship maneuvering
• Safe navigation
• Regular head waves
• Path following

References

1. Abdel-latif, S., Abdel-geliel, M. & Zakzouk E. E. (2013). Simulation of ship maneuvering behavior based on the modular mathematical model. International Conference on Aerospace Sciences and Aviation Technology, 15(Aerospace Sciences & Aviation Technology), Egypt, pp. 1-14. https://doi.org/10.21608/asat.2013.22111
2. Akbar, A., Aasen, A., Msakni, M. K., Fagerholt, K., Lindstad, E. & Meisel, F. (2021). An economic analysis of introducing autonomous ships in a short‐sea liner shipping network. International Transactions in Operational Research, 28(4), 1740-1764. https://doi.org/10.1111/itor.12788
3. Aksu, E., & Köse, E. (2017). Evaluation of mathematical models for tankers’ maneuvering motions. Journal of ETA Maritime Science, 5(1), 95-109. https://dx.doi.org/10.5505/jems.2017.52523
4. Beji, S. (2020). Formulation of wave and current forces acting on a body and resistance of ships. Ocean Engineering, 218, 108121. https://doi.org/10.1016/j.oceaneng.2020.108121
5. Budak, G. (2023). The effect of controller selection in collision avoidance system on fuel consumption for unmanned surface vessel: a case study. Ships and Offshore Structures, 18(2), 315-323. https://doi.org/10.1080/17445302.2022.2157131
6. Çakıcı, F., Kahramanoglu, E., & Alkan, A. D. (2017). Numerical prediction of vertical ship motions and added resistance. Transactions of the Royal Institution of Naval Architects Part A: International Journal of Maritime Engineering, 159(Part A4), 393-402. https://doi.org/10.3940/rina.ijme.2017.a4.450
7. Chen, Y. Y., Ellis-Tiew, M. Z., Chen, W. C., & Wang, C. Z. (2021). Fuzzy risk evaluation and collision avoidance control of unmanned surface vessels. Applied Sciences, 11(14), 6338. https://doi.org/10.3390/app11146338
8. Degre, T., & Lefevre, X. (1981). A collision avoidance system. Journal of Navigation, 34(2), 294-302. https://doi.org/10.1017/S0373463300021408

9. Fang, M. -C., Luo, J. -H., & Lee, M. -L. (2005). A nonlinear mathematical model for ship turning circle simulation in waves. Journal of Ship Research, 49(2), 69-79. https://doi.org/10.5957/jsr.2005.49.2.69
10. Fossen, T. (2002). Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics.
11. He, Y., Li, Z., Mou, J., Hu, W., Li, L., & Wang, B. (2021). Collision-avoidance path planning for multi-ship encounters considering ship manoeuvrability and COLREGs. Transportation Safety and Environment, 3(2), 1-11. https://doi.org/10.1093/tse/tdab004
12. Hirdaris, S., Bai, W., Dessi, D., Ergin, A., Gu, X., Hermundstad, O., Huijsmans, R., Iijima, K., Nielsen, U., Parunov, J., Fonseca, N., Papanikolaou, A., Argyriadis, K., & Incecik, A. (2014). Loads for use in the design of ships and offshore structures. Ocean Engineering, 78, 131–174. https://doi.org/10.1016/j.oceaneng.2013.09.012
13. Jin, Y., Yiew, L., Zheng, Y., Magee, A., Duffy, J., & Chai, S. (2021). Dynamic manoeuvres of KCS with CFD free-running computation and system-based modelling. Ocean Engineering, 241, 110043. https://doi.org/10.1016/j.oceaneng.2021.110043
14. Kafalı, M., & Aksu, E. (2022). A multi-objective optimization model for determining the performance of a sailboat. Journal of ETA Maritime Science, 10(3), 177-184. https://doi.org/10.4274/jems.2022.24392
15. Kobayashi, H., Blok, J., Barr, R., Kim, Y. S., & Nowicki, J. (2002). Specialist committee on Esso Osaka: Final report and recommendations to the 23rd ITTC. 23rd International Towing Tank Conference, pp. 8-14.
16. Kurt, I., & Aymelek, M. (2022). Operational and economic advantages of autonomous ships and their perceived impacts on port operations. Maritime Economics & Logistics 24(2), 302-326. https://doi.org/10.1057/s41278-022-00213-1
17. Lang, X., & Mao, W. (2020). A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements. Ocean Engineering, 209, 107494. https://doi.org/10.1016/j.oceaneng.2020.107494
18. Lee, J. H., Seo, M. G., Park, D. M., Yang, K. K., Kim, K. H., & Kim, Y. (2013). Study on the effects of hull form on added resistance. Proceedings of the 12th International Symposium on Practical Design of Ships and Other Floating Structures. Korea, pp. 329-337.
19. Li, G., & Zhang, X. (2022). Research on the influence of wind, waves, and tidal current on ship turning ability based on Norrbin Model. Ocean Engineering, 259, 111875. https://doi.org/10.1016/j.oceaneng.2022.111875
20. Liu, L., Zhang, L., Zhang, S., & Cao, S. (2020) Multi-UUV cooperative dynamic maneuver decision-making algorithm using intuitionistic fuzzy game theory. Complexity, 2020, 2815258. https://doi.org/10.1155/2020/2815258
21. Liu, S., & Papanikolaou, A. (2016). Fast approach to the estimation of the added resistance of ships in head waves. Ocean Engineering, 112, 211-225. https://doi.org/10.1016/j.oceaneng.2015.12.022
22. Liu, S., & Papanikolaou, A. (2017). Approximation of the added resistance of ships with small draft or in ballast condition by empirical formula. Proceedings of the IMechE Part M: Journal of Engineering for the Maritime Environment, 223(1), 27-40. https://doi.org/10.1177/1475090217710099
23. Lyu, H. G., & Yin, Y. (2017). Ship’s trajectory planning for collision avoidance at sea based on modified artificial potential field. 2nd International Conference on Robotics and Automation Engineering, (Icrae) Shanghai, pp. 351-357.
24. Mohamed-Seghir, M., Kula, K., & Kouzou, A. (2021). Artificial intelligence-based methods for decision support to avoid collisions at sea. Electronics, 19(10), 2360. https://doi.org/10.3390/electronics10192360
25. Moreira, L., Fossen, T. I., & Soares, C. G. (2007) Path following control system for a tanker ship model. Ocean Engineering, 34, (14,15), 2074-2085. https://doi.org/10.1016/j.oceaneng.2007.02.005
26. Ogawa, A., & Kasai, H. (1978). On the mathematical model of maneuvering motion of ships. International Shipbuild Progress, 25, 306–319.
27. Ozdemir, Y., & Barlas, B. (2017). Numerical study of ship motions and added resistance in regular incident waves of KVLCC2 model. International Journal of Naval Architecture and Ocean Engineering, 9, 149-159. https://doi.org/10.1016/j.ijnaoe.2016.09.001
28. Park, D. -M. Kim, Y., Seo, M. -G., & Lee, J. (2016). Study on added resistance of a tanker in head waves at different drafts. Ocean Engineering, 111, 569-581. https://doi.org/10.1016/j.oceaneng.2015.11.026
29. Perera, L. P., Carvalho, J. P., & Soares, C. G. (2010). Bayesian network based sequential collision avoidance action execution for an ocean navigational system. IFAC Proceedings, 43(20), 266-271.
30. Praczyk, T. (2015). Neural anti-collision system for autonomous surface vehicle. Neurocomputing, 149, 559–572. https://doi.org/10.1016/j.neucom.2014.08.018
31. Sadat-Hosseini, H., Wu, P., Carrica, P., Kim, H., Toda, Y., & Stern, F. (2013). CFD verification and validation of added resistance and motions of KVLCC2 with fixed and free surge in short and long head waves. Ocean Engineering, 59, 240–273. https://doi.org/10.1016/j.oceaneng.2012.12.016
32. Suo, Y., Chen, W., Claramunt, C., & Yang, S. (2020). A ship trajectory prediction framework based on a recurrent neural network. Sensors, 20, 5133. https://doi.org/10.3390/s20185133
33. Szelangiewicz, T., Wiśniewski, B., & Żelazny, K. (2014). The influence of wind, wave and loading condition on total resistance and speed of the vessel. Polish Maritime Research, 21(3), 61-67. https://doi.org/10.2478/pomr-2014-0031
34. Tsou, M. C., & Hsueh, C. K. (2010). The study of ship collision avoidance route planning by ant colony algorithm. Journal of Marine Science and Technology, 18(5), 746-756. https://doi.org/10.51400/2709-6998.1929
35. Tsujimoto, M., Shibata, K., & Kuroda, M. (2008). A practical correction method for added resistance in waves. Journal of the Japan Society of Naval Architects and Ocean Engineers, 8, 177-184. https://doi.org/10.2534/jjasnaoe.8.177
36. Wang, C., & Fu, Y. (2020). Ship trajectory prediction based on attention in bidirectional recurrent neural networks. 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, China, pp. 529–533.
37. Xie, Z., Falzarano, J. M., & Wang, H. (2020). A framework of numerically evaluating a maneuvering vessel in waves. Journal of Marine Science and Engineering, 8(6), 392. https://doi.org/10.3390/jmse8060392
38. Yasukawa, H., & Sakuno, R. (2019). Application of the mmg method for the prediction of steady sailing condition and course stability of a ship under external disturbances. Journal of Marine Science and Technology, 25, 196–220. https://doi.org/10.1007/s00773-019-00641-4
39. Yasukawa, H., & Yoshimura, Y. (2015). Introduction of mmg standard method for ship maneuvering predictions. Journal of Marine Science and Technology, 20, 37–52. https://doi.org/10.1007/s00773-014-0293-y
40. Zaccone, R., & Martelli, M. (2020). A collision avoidance algorithm for ship guidance applications. Journal of Marine Engineering & Technology, 19, 62-75. https://doi.org/10.1080/20464177.2019.1685836
41. Zhao, W., Wang, Y., Zhang, Z., & Wang, H. (2021). Multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm. Journal of Marine Science and Engineering, 9(4), 357. https://doi.org/10.3390/jmse9040357
42. Zhou, Z., Zhang, Y., & Wang, S. A. (2021). Coordination system between decision making and controlling for autonomous collision avoidance of large intelligent ships. Journal of Marine Science and Engineering, 9(11), 1202. https://doi.org/10.3390/jmse9111202