Some issues of increasing the energy efficiency of ships by improving navigation methods

Authors

Yevgeniy Kalinichenko (ed)
Odesa National Maritime University
https://orcid.org/0000-0003-2898-7313
Keywords: Energy efficiency, ship navigation, route planning, fuel consumption, maneuvering, meteorological support, ECDIS, navigation safety, risk assessment, adaptive control, autonomous ships, propulsion systems, environmental impact, voyage optimization, underwater cargo vessels, digital strategies, artificial intelligence, condition monitoring

Synopsis

This monograph presents a comprehensive exploration of scientific and practical approaches to increasing the energy efficiency of maritime transport through the optimization of navigation methods. Against the backdrop of global efforts to reduce greenhouse gas emissions and rising fuel costs, the study offers a multidisciplinary framework that addresses key operational, technical, and digital strategies for minimizing fuel consumption across various ship operations. The work is structured into thematic chapters that sequentially build an integrated understanding of energy-efficient navigation. Strategic models of ship control are proposed, focusing on minimizing route deviations through precise measurements and mathematical error decomposition. Route optimization under meteorological conditions is examined using advanced software systems, while the interplay between navigational risk and energy use is analyzed through multi-criteria decision-making approaches. Particular emphasis is placed on vessel interaction scenarios, mooring operations, and port maneuvering, where energy-intensive auxiliary processes are analyzed through risk-based methodologies. Artificial intelligence is explored as a transformative tool for course-keeping and trajectory control, enabling significant gains in fuel efficiency and safety. The potential of underwater cargo transport and its energy advantages is introduced, alongside the integration of meteorological and hydrographic support systems using satellite and in-situ data. Inland waterway systems are considered as case studies for applying intelligent monitoring and real-time energy-navigational assessments. This monograph represents the consolidated result of a three-year research project entitled "Theory and Practice of Energy Efficiency Management of a Marine Vessel", carried out by the Department of Navigation and Control of the Ship at Odesa National Maritime University (Registration No. 0122U201366). The research findings offer a unified model for improving maritime energy efficiency through smarter navigation and are intended for researchers, maritime practitioners, and policymakers working toward sustainable development in the shipping sector.

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How to Cite: Kalinichenko, Y., Vasalatii, N., Rossomakha, O., Koliesnik, O., Sagaydak, O., Oberto Santana, L. et al.; Kalinichenko, Y. (Ed.) (2025). Some issues of increasing the energy efficiency of ships by improving navigation methods. Tallinn: Scientific Route OÜ. doi: https://doi.org/10.21303/978-9908-9706-4-6

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Indexing:

arch engpaper   scilit dimen ester   ouci Zenodo45 openaire45 imgonline com ua Resize H8EcTyBw1Q4MCFJW


Introduction. Some issues of increasing the energy efficiency of ships by improving navigation methods
Yevgeniy Kalinichenko

Chapter 1. A strategic approach to energy-efficient methods of navigation, maneuvering and ship control (6-27)
Yevgeniy Kalinichenko

Chapter 2. Energy-efficient ship route planning considering meteorological navigation conditions (28-45)
Nadiia Vasalatii

Chapter 3. Consideration and assessment of navigational risks to improve energy-efficient ship management (46-60)
Olena Rossomakha

Chapter 4. Choosing the best maneuver for vessel separation taking into account the energy efficiency of the trajectory (61-78)
Oleksandr Koliesnik

Chapter 5. Analysis of possible risks, which affect energy efficiency of the ship while maneuvering and mooring (79-93)
Oleksandr Sagaydak

Chapter 6. Modern approaches to maritime navigation: integrating artificial intelligence into ship course-keeping systems (94-114)
Leonid Oberto Santana

Chapter 7. Analysis of modern underwater navigation and design capabilities of underwater cargo vessels (115-137)
Anastasiia Zaiets

Chapter 8. Meteorological and hydrographic support of energy-saving maritime transport (138-158)
Georgiy Tomchakovsky

Chapter 9. Development of a system for assessing navigational and energy safety on inland waterways (159-177)
Nataliia Dolynska

Chapter 10. Digital strategies for enhancing the efficiency of cargo ships maintenance (178-196)
Andrii Holovan

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Author Biographies

Yevgeniy Kalinichenko, Odesa National Maritime University

PhD, Associate Professor, Head of Department
Department of Navigation and Control of the Ship
https://orcid.org/0000-0003-2898-7313
Corresponding author
kalinichenko.yevgeniy1964@gmail.com

Nadiia Vasalatii, Odesa National Maritime University

PhD, Associate Professor
Department of Navigation and Control of the Ship
https://orcid.org/0000-0002-7188-9922

Olena Rossomakha, Odesa National Maritime University

PhD, Associate Professor
Department of Navigation and Control of the Ship
https://orcid.org/0000-0002-4425-2192

Oleksandr Koliesnik, Odesa National Maritime University

Senior Lecturer
Department of Navigation and Control of the Ship
https://orcid.org/0009-0003-3713-2015

Oleksandr Sagaydak, Odesa National Maritime University

Senior Lecturer
Department of Navigation and Control of the Ship
https://orcid.org/0000-0002-8294-8828

Leonid Oberto Santana, Odesa National Maritime University

Senior Lecturer
Department of Navigation and Control of the Ship
https://orcid.org/0009-0009-4407-3766

Anastasiia Zaiets, Odesa National Maritime University

PhD, Associate Professor
Department of Shipbuilding and Ship Repair named after Prof. Y. L. Vorobyov
https://orcid.org/0000-0002-5803-9069

Georgiy Tomchakovsky, Odesa National Maritime University

Senior Lecturer
Department of Navigation and Control of the Ship
https://orcid.org/0000-0002-9799-4368

Nataliia Dolynska, Odesa National Maritime University

Postgraduate Student
Department of Navigation and Control of the Ship
https://orcid.org/0009-0003-1347-0538

Andrii Holovan, Odesa National Maritime University

Doctor of Technical Sciences, Associate Professor
Department of Navigation and Maritime Safety
https://orcid.org/0000-0001-6589-4381

References

Emissions from planes and ships: facts and figures (infographic) (2019). EU Parliament. Available at https://www.europarl.europa.eu/news/en/headlines/society/20191129STO67756/emissions-from-planes-and-ships-facts-and-figures-infographic

Tuswan, T., Misbahudin, S., Junianto, S., Yudo, H., Budi Santosa, A. W., Trimulyono, A. et al. (2022). Current research outlook on solar-assisted new energy ships: representative applications and fuel & GHG emission benefits. IOP Conference Series: Earth and Environmental Science, 1081 (1), 012011. https://doi.org/10.1088/1755-1315/1081/1/012011

Miller, G. (2022). Ship fuel spikes to historic $1,000/ton mark as war fallout worsens. FreightWaves. Available at: https://www.freightwaves.com/news/russian-invasion-propels-price-of-ship-fuel-to-historic-high

Hu, P., Xie, Q., Ma, C., Zhang, G. (2020). Silicone-Based Fouling-Release Coatings for Marine Antifouling. Langmuir, 36 (9), 2170–2183. https://doi.org/10.1021/acs.langmuir.9b03926

Ng, C., Tam, I. (2019). Overview of Waste Heat Recovery Technologies for Maritime Applications. Society of Naval Architects and Marine Engineers. Singapre, 64. Available at https://www.researchgate.net/publication/341069756_Overview_of_Waste_Heat_Recovery_Technologies_for_Maritime_Applications

Damian, S. E., Wong, L. A., Shareef, H., Ramachandaramurthy, V. K., Chan, C. K., Moh, T. S. Y., Tiong, M. C. (2022). Review on the challenges of hybrid propulsion system in marine transport system. Journal of Energy Storage, 56, 105983. https://doi.org/10.1016/j.est.2022.105983

Willumsen, T. (Ed.) (2021). Cleaner Shipping: Air pollution, climate, technical solutions and regulation. Green Transition Denmark. Available at https://rgo.dk/wp-content/uploads/GTD_Cleaner_shipping_2021_Final.pdf

Ortolani, F., Dubbioso, G. (2020). In-plane and single blade loads measurement setups for propeller performance assessment during free running and captive model tests. Ocean Engineering, 217, 107928. https://doi.org/10.1016/j.oceaneng.2020.107928

Lu, R., Turan, O., Boulougouris, E., Banks, C., Incecik, A. (2015). A semi-empirical ship operational performance prediction model for voyage optimization towards energy efficient shipping. Ocean Engineering, 110, 18–28. https://doi.org/10.1016/j.oceaneng.2015.07.042

Guidelines on the method of calculation of the attained energy efficiency design index (EEDI) for new ships (2022). IMO. Available at: https://wwwcdn.imo.org/localresources/en/KnowledgeCentre/IndexofIMOResolutions/MEPCDocuments/MEPC.364%2879%29.pdf

Von Knorring, H. J., Andersson, K. (2011). The Energy Efficiency Gap in Shipping: Barriers to Improvement. International Association of Maritime Economists (IAME) Conference. Santiago de Chile. Available at: https://www.researchgate.net/publication/235874758_The_Energy_Efficiency_Gap_in_Shipping_-_Barriers_to_Improvement

Rudzki, K., Gomulka, P., Hoang, A. T. (2022). Optimization Model to Manage Ship Fuel Consumption and Navigation Time. Polish Maritime Research, 29 (3), 141–153. https://doi.org/10.2478/pomr-2022-0034

Review of Maritime Transport (2022). United Nations Conference on Trade and Development (UNCTAD). Available at: https://unctad.org/system/files/official-document/rmt2022_en.pdf

Vorokhobyn, I. I. (2019). Razrabotka teoryy y metodov otsenky y povyshenyia nadezhnosty sudovozhdenyia. Odesa: NU «OMA», 308.

Sommer, K. D., Harris, P., Eichstädt, S., Füssl, R., Dorst, T., Schütze, A. et al. (2023). Modelling of networked measuring systems--from white-box models to data based approaches. arXiv:2312.13744. https://doi.org/10.48550/arXiv.2312.13744

Astaikin, D. V., Alekseichuk, B. M. (2014). Identifikatciia zakonov raspredeleniia navigatcionnykh pogreshnostei smeshannymi zakonami dvukh tipov Avtomatyzatsiia sudovykh tekhnichnykh zasobiv, 20, 3–9.

Standards for Hydrographic Surveys (S-44) (2023). International Hydrographic Organization. Available at: https://iho.int/en/standards-and-specifications

Yang, Z., Qu, W., Zhuo, J. (2024). Optimization of Energy Consumption in Ship Propulsion Control under Severe Sea Conditions. Journal of Marine Science and Engineering, 12 (9), 1461. https://doi.org/10.3390/jmse12091461

Report: Sustainable fuels for shipping by 2050 – the 3 key elements of success (2024). Wärtsilä. Available at: https://www.wartsila.com/insights/whitepaper/sustainable-fuels-for-shipping-by-2050-industry-report

Bowditch, N. (2017). The American Practical Navigator. National Geospatial-Intelligence Agency.

E-navigation Strategy Implementation Plan (2019). International Maritime Organization.

DTN Weather Intelligence & Insights for Confident Offshore Decisions. Available at: https://www.dtn.com/weather/marine-and-offshore/explore/#products

Services for Each Market. Weathernews Inc. Available at: https://global.weathernews.com/your-industry/

The Modeling, Analysis, Predictions and Projections Program Mission. Modeling, Analysis, Predictions and Projections. Available at: https://cpo.noaa.gov/divisions-programs/earth-system-science-and-modeling-division/modeling-analysis-predictions-and-projections/

Latinopoulos, C., Zavvos, E., Kaklis, D., Leemen, V., Halatsis, A. (2025). Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning. Journal of Marine Science and Engineering, 13 (5), 902. https://doi.org/10.3390/jmse13050902

Mason, J., Larkin, A., Gallego-Schmid, A. (2023). Mitigating stochastic uncertainty from weather routing for ships with wind propulsion. Ocean Engineering, 281, 114674. https://doi.org/10.1016/j.oceaneng.2023.114674

Fourth IMO GHG Study 2020 (2020). International Maritime Organization. Available at: https://greenvoyage2050.imo.org/wp-content/uploads/2021/07/Fourth-IMO-GHG-Study-2020-Full-report-and-annexes_compressed.pdf

Bryden, H. L., Stommel, H. (1982). Origin of the Mediterranean Outflow. Journal of Marine Research, 40, 55–71.

World Ocean Database Select and Search. National Centers for Environmental Information. Available at: https://www.ncei.noaa.gov/access/world-ocean-database-select/dbsearch.html

Datasets. Copernicus Climate Data Store. Available at: https://cds.climate.copernicus.eu/datasets

Review of Maritime Transport 2023 (2023). United Nations Conference on Trade and Development. Available at: https://unctad.org/webflyer/review-maritime-transport-2023

Hetherington, C., Flin, R., Mearns, K. (2006). Safety in shipping: The human element. Journal of Safety Research, 37 (4), 401–411. https://doi.org/10.1016/j.jsr.2006.04.007

Chauvin, C. (2011). Human Factors and Maritime Safety. Journal of Navigation, 64 (4), 625–632. https://doi.org/10.1017/s0373463311000142

Reason, J. (2016). Managing the Risks of Organizational Accidents. London: Routledge, 272. https://doi.org/10.4324/9781315543543

Wang, N., Chang, D., Yuan, J., Shi, X., Bai, X. (2020). How to maintain the safety level with the increasing capacity of the fairway: A case study of the Yangtze Estuary Deepwater Channel. Ocean Engineering, 216, 108122. https://doi.org/10.1016/j.oceaneng.2020.108122

Lu, C.-S., Tsai, C.-L. (2008). The effects of safety climate on vessel accidents in the container shipping context. Accident Analysis & Prevention, 40 (2), 594–601. https://doi.org/10.1016/j.aap.2007.08.015

International Maritime Organization. Available at: https://www.imo.org/

DNV. Available at: https://www.dnv.com

Updated IMO amendments bring sustainability to forefront. Bureau Veritas. Available at: https://marine-offshore.bureauveritas.com/updated-imo-amendments-bring-sustainability-forefront

Jon, M. H., Yu, C. I. (2023). Optimization of Ship Energy Efficiency Considering Navigational Environment and Safety. Proceedings of the 5th International Conference on Numerical Modelling in Engineering, 1–15. https://doi.org/10.1007/978-981-99-0373-3_1

International Hydrographic Organization. Available at: https://iho.int/

Stopford, M. (2008). Maritime Economics 3e. London: Routledge, 840. https://doi.org/10.4324/978020389174

Maersk. Available at: https://www.maersk.com

ISO 31000:2018. Risk management – Guidelines (2018). International Organization for Standardization.

NAPA. Voyage Optimization Systems. Available at: https://www.napa.fi/solutions/voyage-optimization/

Torskyi, V., Rossomakha, O., Oberto Santana, L. (2024). Systematic risk assessment and management in modern shipping: a comprehensive approach to safety analysis. Boston: Primedia eLaunch, 114.

DSTU ISO 31000:2018. Menedzhment ryzykiv. Pryntsypy ta nastanovy (ISO 31000:2018, IDT) (2019). Kyiv: DP «UkrNDNTs», 30.

IMO. ECDIS – Guidance for Good Practice: MSC.1/Circ.1503/Rev.1 (2017). London, 32.

S-52 – Specifications for Chart Content and Display Aspects of ECDIS (2020). International Hydrographic Organization. Monaco, 142.

Raymarine. Radar Integration Manual (2021). Fareham: Raymarine Ltd., 87.

NAVI-SAILOR 3000 ECDIS-I (Version 4.00.07). User Manual. Transas Ltd. Available at: https://www.scribd.com/doc/72490384/Transas-3000i-Operation

Lund, M. S., Gulland, J. E., Hareide, O. S., Josok, Eyvind, Weum, K. O. C. (2018). Integrity of Integrated Navigation Systems. 2018 IEEE Conference on Communications and Network Security (CNS), 1–5. https://doi.org/10.1109/cns.2018.8433151

Lazarowska, A. (2012). Decision support system for collision avoidance at sea. Polish Maritime Research, 19, 19–24. https://doi.org/10.2478/v10012-012-0018-2

Stateczny, A., Lisaj, A. (2006). Radar and AIS Data Fusion for the Needs of the Maritime Navigation. 2006 International Radar Symposium, 1–4. https://doi.org/10.1109/irs.2006.4338054

Godet, A., Nurup, J. N., Saber, J. T., Panagakos, G., Barfod, M. B. (2023). Operational cycles for maritime transportation: A benchmarking tool for ship energy efficiency. Transportation Research Part D: Transport and Environment, 121, 103840. https://doi.org/10.1016/j.trd.2023.103840

Fagerholt, K., Gausel, N. T., Rakke, J. G., Psaraftis, H. N. (2015). Maritime routing and speed optimization with emission control areas. Transportation Research Part C: Emerging Technologies, 52, 57–73. https://doi.org/10.1016/j.trc.2014.12.010

Hu, L., Naeem, W., Rajabally, E., Watson, G., Mills, T., Bhuiyan, Z., Salter, I. (2017). COLREGs-Compliant Path Planning for Autonomous Surface Vehicles: A Multiobjective Optimization Approach. IFAC-PapersOnLine, 50 (1), 13662–13667. https://doi.org/10.1016/j.ifacol.2017.08.2525

He, H., Mansuy, M., Verwilligen, J., Delefortrie, G., Lataire, E. (2024). Global path planning for inland vessels based on fast marching algorithm. Ocean Engineering, 312, 119172. https://doi.org/10.1016/j.oceaneng.2024.119172

Stopford, M. (2009). Maritime Economics. London: Routledge, 815.

Wang H., Meng Q., Liu Z (2021). A Review of Risk Assessment Methods for Maritime Traffic. International Core Journal of Engineering, 7 (6), 28–36. https://doi.org/10.6919/ICJE.202106_7(6).0005

Vincenty, T. (1975). Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey Review, 23 (176), 88–93. https://doi.org/10.1179/sre.1975.23.176.88

Aven, T., Renn, O. (2010). Risk management and governance: Concepts, guidelines and applications. Vol. 16. Springer, 278. https://doi.org/10.1007/978-3-642-13926-0

Goerlandt, F., Montewka, J. (2015). Maritime transportation risk analysis: Review and analysis in light of some foundational issues. Reliability Engineering & System Safety, 138, 115–134. https://doi.org/10.1016/j.ress.2015.01.025

Roșca, E., Raicu, S., Roșca, M., Rusca, F. V. (2014). Risks and Reliability Assessment in Maritime Port Logistics. Advanced Materials Research, 1036, 963–968. https://doi.org/10.4028/www.scientific.net/amr.1036.963

Sustainable Development Report (2016). China Navigation Co. Available at: https://www.swire.com/en/sustainability/sd_reports/cnco_2016.pdf

Marine Environment Protection Committee (MEPC 82) (2024). International Maritime Organization. Available at https://www.imo.org/en/MediaCentre/MeetingSummaries/Pages/MEPC-82nd-session.aspx

Eighty-third session of the Marine Environment Protection Committee (2025). International Maritime Organization. Available at: https://idocs.rs-class.org/calend/MEPC%2083.pdf

Sahaidak, O., Melnyk, O. (2024). Analysis and assessment of new risk factors in the shipping industry. Shipping & Navigation, 36, 147–162. Available at: https://navjournal-nuoma.learnmarine.com/wp-content/uploads/2025/01/36-2024_O.-Sagaydak-O.-Melnyk-Analysis-and-assessment-of-new-risk-factors-in-the-shipping-industry.pdf

Moreno Parra, N., Nagi, A., Kersten, W. (2018). Risk Assessment Methods in Seaports: A Literature Review. Publications of the HAZARD project. https://doi.org/10.13140/RG.2.2.32359.50081

Guidelines for formal safety assessment (FSA) for use in the IMO rule-making process (MSC/Circ.1023 – MEPC/Circ.392) (2002). London: IMO. Available at: https://wwwcdn.imo.org/localresources/en/OurWork/HumanElement/Documents/1023-MEPC392.pdf

Annual overview of marine casualties and incidents (Accident Investigation Publication) (2024). Lisbon: EMSA. Available at: https://www.emsa.europa.eu/accident-investigation-publications/annual-overview.html

Kalinichenko, G., Torskyi, V., Sagaydak, O. (2024). Rapid assessment of risks in tug operations within port areas. Technology Transfer: Fundamental Principles and Innovative Technical Solutions, 18–21. https://doi.org/10.21303/2585-6847.2024.003569

Sagaydak, O., Kucherenko, V., Kotenko, O., Prokhorov, V., Melnyk, O. (2024). Innovative technologies and regulatory measures to reduce environmental risks in the shipping industry. Materialy konferentsii MTsND. Odesa, 181–186. Available at: https://archives.mcnd.org.ua/index.php/conference-proceeding/article/view/190

Melnyk, O., Sagaydak, O., Voloshyn, A., Lebedieva, L., Radchenko, I. (2024). Risk management and its impact on efficiency of ship operations. Grail of Science, 45, 150–161. https://doi.org/10.36074/grail-of-science.01.11.2024.016

Sagaydak, O. (2021). Concept of optimization of ship-port-cargo interface, taking into account existing risk assessment methods and use of electronic technologies. Transport Development, 2 (9), 64–77. https://doi.org/10.33082/td.2021.2-9.05

Sagaydak, O. I. (2022). Check-list method and concept of round-circle risk assessment of ship’s mooring. Collection of Scientific Publications NUS, 488 (1), 89–95. https://doi.org/10.15589/znp2022.1(488).12

Sagaydak, O. I. (2024). Model of circular risk assessment of ship’s mooring operation using information technology. Transport Development, 23 (4), 29–41. https://doi.org/10.33082/td.2024.4-23.03

Sagaydak, O., Pulyaev, I., Kotenko, O., Prokhorov, V., Melnyk, O. (2024). Development of a risk assessment algorithm to improve safety of ship mooring operations. Collection of Scientific Papers «ΛΌГOΣ». Paris, 155–158. https://doi.org/10.36074/logos-20.09.2024.030

Bibuli, M., Odetti, A., Zereik, E. (2019). Adaptive steering control for an azimuth thrusters-based autonomous vessel. Journal of Marine Engineering & Technology, 19, 76–91. https://doi.org/10.1080/20464177.2019.1707386

Zaiets, A. Yu., Oberto Santana, L. E. (2025) Energy efficiency of modern propulsion systems: comparative analysis of traditional and azimuthal thrusters in the context of ocean shipbuilding. Water Transport, 1 (42), 93–98. https://doi.org/10.33298/2226-8553.2025.1.42.13

Fossen, T. I. (2021). Handbook of Marine Craft Hydrodynamics and Motion Control. Chichester: John Wiley & Sons Ltd. https://doi.org/10.1002/9781119994138

Golikov, V., Siniuta, K. (2022). Main approaches to ship traffic control on course. Transport Systems and Technologies, (39), 209–215. https://doi.org/10.32703/2617-9040-2022-39-19

Golikov, V. V., Siniuta, K. O. (2024). Deep learning in the context of artificial intelligence in marine navigation: development prospects. Water Transport, 1 (39), 104–111. https://doi.org/10.33298/2226-8553.2024.1.39.10

Azipod® CO product introduction (2010). ABB Group. Helsinki. Available at: https://library.e.abb.com/public/933fa7cfa4392993c1257b1a005b78e9/Azipod%20CO_Product%20Introduction.pdf

Guan, W., Peng, H., Zhang, X., Sun, H. (2022). Ship Steering Adaptive CGS Control Based on EKF Identification Method. Journal of Marine Science and Engineering, 10 (2), 294. https://doi.org/10.3390/jmse10020294

Shi, X., Chen, P., Chen, L. (2023). An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller. Journal of Marine Science and Engineering, 11 (12), 2294. https://doi.org/10.3390/jmse11122294

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y. et al. (2016). Continuous control with deep reinforcement learning. Proceedings of the International Conference on Learning Representations (ICLR 2016). arXiv preprint. https://doi.org/10.48550/arXiv.1509.02971

Perera, L. P. (2019). Deep Learning Toward Autonomous Ship Navigation and Possible COLREGs Failures. Journal of Offshore Mechanics and Arctic Engineering, 142 (3). https://doi.org/10.1115/1.4045372

Vukić, Z., Omerdić, E., Kuljača, L. (1997). Fuzzy Autopilot for Ships Experiencing Shallow Water Effect in Manoeuvering. IFAC Proceedings Volumes, 30 (22), 99–104. https://doi.org/10.1016/s1474-6670(17)46497-4

Volyanskyy, S., Vorokhobin, I., Volyanskaya, Y., Mazur, O., Onishchenko, O. (2022). Marine ship’s course stabilization based on an autopilot with a simple fuzzy controller. Scientific Bulletin of Naval Academy, XXV (1), 23–35. https://doi.org/10.21279/1454-864x-22-i1-003

Unar, S., Unar, M. A., Memon, Z. A., Narejo, S. (2022). A neural network based heading and position control system of a ship. Mehran University Research Journal of Engineering and Technology, 41 (2), 172–177. https://doi.org/10.22581/muet1982.2202.16

Lee, S.-D., Tzeng, C.-Y., Huang, W.-W. (2013). Ship steering autopilot based on ANFIS framework and conditional tuning scheme. Marine Engineering Frontiers, 1 (3), 53–62. Available at: https://www.academia.edu/27933110/Ship_Steering_Autopilot_Based_on_ANFIS_Framework_and_Conditional_Tuning_Scheme

Omerdic, E., Roberts, G. N., Vukic, Z. (2003). A fuzzy track-keeping autopilot for ship steering. Journal of Marine Engineering & Technology, 2 (1), 23–35. https://doi.org/10.1080/20464177.2003.11020163

Zhang, Q., Jiang, N., Hu, Y., Pan, D. (2017). Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks. International Journal of E-Navigation and Maritime Economy, 7, 34–41. https://doi.org/10.1016/j.enavi.2017.06.004

Rigatos, G., Tzafestas, S. (2006). Adaptive fuzzy control for the ship steering problem. Mechatronics, 16 (8), 479–489. https://doi.org/10.1016/j.mechatronics.2006.01.003

Miller, A., Walczak, S. (2020). Maritime Autonomous Surface Ship’s Path Approximation Using Bézier Curves. Symmetry, 12 (10), 1704. https://doi.org/10.3390/sym12101704

Zhi, H., Pan, B., Zhu, G. (2023). Event Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data. Applied Sciences, 13 (18), 10035. https://doi.org/10.3390/app131810035

Li, S., Liu, T., Liu, J., Hu, X., Shen, W. (2024). Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy. Journal of Marine Engineering & Technology, 24 (3), 179–193. https://doi.org/10.1080/20464177.2024.2423418

Kinsey, J. C., Smallwood, D. A., Whitcomb, L. L. (2003). A new hydrodynamics test facility for UUV dynamics and control research. Proceedings of the IEEE/MTS OCEANS Conference. San Diego, 356–361. https://doi.org/10.1109/oceans.2003.178587

Bingham, B., Seering, W. (2006). Hypothesis Grids: Improving Long Baseline Navigation for Autonomous Underwater Vehicles. IEEE Journal of Oceanic Engineering, 31 (1), 209–218. https://doi.org/10.1109/joe.2006.872220

Dubrovin, F., Vaulin, Y., Scherbatyuk, A., Scherbatyuk, D., Rodionov, A. (2020). Navigation for AUV, Located in the Shadow Area of LBL, During the Group Operations. Global Oceans 2020: Singapore – U.S. Gulf Coast, 1–6. https://doi.org/10.1109/ieeeconf38699.2020.9389418

Topini, E., Fanelli, F., Topini, A., Pebody, M., Ridolfi, A., Phillips, A. B., Allotta, B. (2023). An experimental comparison of Deep Learning strategies for AUV navigation in DVL-denied environments. Ocean Engineering, 274, 114034. https://doi.org/10.1016/j.oceaneng.2023.114034

Petritoli, E., Leccese, F. (2018). High Accuracy Attitude and Navigation System for an Autonomous Underwater Vehicle (AUV). ACTA IMEKO, 7 (2), 3. https://doi.org/10.21014/acta_imeko.v7i2.535

Jouffroy, Jé., Opderbecke, J. (2007). Underwater Vehicle Navigation Using Diffusion-Based Trajectory Observers. IEEE Journal of Oceanic Engineering, 32 (2), 313–326. https://doi.org/10.1109/joe.2006.880392

Zhang L., Wu S., Tang C. (2023). Cooperative Positioning of Underwater Unmanned Vehicle Clusters Based on Factor Maps. https://doi.org/10.2139/ssrn.4517598

Murphy, R. R. (2000). Introduction to AI Robotics. Cambridge: MIT Press, 466.

Moniruzzaman, M., Islam, S. M. S., Bennamoun, M., Lavery, P. (2017, November). Deep learning on underwater marine object detection: a survey. International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2017). Cham: Springer, 150–160. https://doi.org/10.1007/978-3-319-70353-4_13

Yuan, J., Wang, H., Zhang, H., Lin, C., Yu, D., Li, C. (2021). AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning. Journal of Marine Science and Engineering, 9 (11), 1166. https://doi.org/10.3390/jmse9111166

Manicacci, F.-M., Mourier, J., Babatounde, C., Garcia, J., Broutta, M., Gualtieri, J.-S., Aiello, A. (2022). A Wireless Autonomous Real-Time Underwater Acoustic Positioning System. Sensors, 22 (21), 8208. https://doi.org/10.3390/s22218208

Kim, S., Choi, J. (2017). Optimal Deployment of Sensor Nodes Based on Performance Surface of Underwater Acoustic Communication. Sensors, 17 (10), 2389. https://doi.org/10.3390/s17102389

Porter, M. B. (2016). BELLHOP3D User Guide. Heat, Light & Sound Research. Available at: https://usermanual.wiki/Document/Bellhop3D20User20Guide202016725.915656596.pdf

Komissarova, N. N. (1998). Horizontal refraction of rays in the coastal zone for different sound speed profiles. Acoustical Journal (Akusticheskii Zhurnal), 6, 801–807.

Li, Y., Wang, S., Jin, C., Zhang, Y., Jiang, T. (2019). A Survey of Underwater Magnetic Induction Communications: Fundamental Issues, Recent Advances, and Challenges. IEEE Communications Surveys & Tutorials, 21 (3), 2466–2487. https://doi.org/10.1109/comst.2019.2897610

Tonolini, F., Adib, F. (2018). Networking across boundaries. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, 117–131. https://doi.org/10.1145/3230543.3230580

Tactical Undersea Network Architectures (TUNA). DARPA. Available at: https://www.darpa.mil/research/programs/tactical-undersea-network-architectures

Ali, M. F., Jayakody, D. N. K., Chursin, Y. A., Affes, S., Dmitry, S. (2019). Recent Advances and Future Directions on Underwater Wireless Communications. Archives of Computational Methods in Engineering, 27 (5), 1379–1412. https://doi.org/10.1007/s11831-019-09354-8

Zhao, A. (2022). Palaeoclimate modelling of monsoons during past warm periods. [Doctoral dissertation; University College London]. Available at: https://discovery.ucl.ac.uk/id/eprint/10166788/

Gladkikh, I. I., Kapochkin, B. B., Kucherenko, N. V., Lisovodsky, V. V. (2007). Formuvannia pohodnykh umov v morskykh ta pryberezhnykh raionakh. Odesa: Astroprint.

Kapochkin, B. B., Dolya, V. D. (2006). Atmospheric processes as a reflection of the gravitational field and variabilit. Proceedings of the 1st All Ukrainian Congress of Ecologists. Vinnytsia: Vinnytsia National Technical University, 50. https://doi.org/10.13140/RG.2.1.1863.1521

Konkin, V. V., Kapochkin, B. B., Dolya, V. D. (2008). The impact of geodynamic processes in atmospheric circulation. Geography and Reality. Scientific Record of the National Pedagogical University named after M. P. Dragomanova, 4 (19), 37–44.

Davis, R. E. (1985). Drifter observations of coastal surface currents during CODE: The method and descriptive view. Journal of Geophysical Research: Oceans, 90 (C3), 4741–4755. https://doi.org/10.1029/jc090ic03p04741

Dolia, V. D. (2008). The gravitational theory of baric formations. Geophysical Research Abstracts, 10. https://doi.org/10.13140/RG.2.1.1584.0487

Wang, P. X., Wang, B., Cheng, H., Fasullo, J., Guo, Z., Kiefer, T., Liu, Z. (2017). The global monsoon across time scales: Mechanisms and outstanding issues. Earth-Science Reviews, 174, 84–121. https://doi.org/10.1016/j.earscirev.2017.07.006

Ramage, C. S. (1971). Monsoon meteorology. New York: Academic Press. University of Hawaii. Available at: https://ia601502.us.archive.org/29/items/in.ernet.dli.2015.120147/2015.120147.Monsoon-Meteorology_text.pdf

Climate Prediction Center. National Weather Service. NOAA. Available at: https://www.cpc.ncep.noaa.gov/

NASA gravity map of Earth (2022). NASA. Available at: https://mapofsouthwesternontario.pages.dev/posts/nasa-gravity-map-of-earth

Kapochkin, B. B., Kucherenko, N. V., Lisovodskyi, V. V., Konkin, V. V. (2003). Fizychni mekhanizmy vyrivniuvannia barychnykh hradiientiv v atmosferi. Kyiv: HNTB Ukrainy, 12.

Dolya, V. D. (2014). Monsoons, as part of the global circulation of the Earth's atmosphere, the geophysical nature of the phenomenon. Shevchenko Spring – 2014. Part 3: Geography. Kyiv, 93.

Uchitel, I. L., Dorofeev, V. S., Iaroshenko, V. N., Kapochkin, B. B. Geodinamika. Osnovy dinamicheskoi geodezii. Odesa: Astroprint, 312.

Pavlis, N. K., Holmes, S. A., Kenyon, S. C., Factor, J. K. (2012). The development and evaluation of the Earth Gravitational Model 2008 (EGM2008). Journal of Geophysical Research: Solid Earth, 117 (B4). https://doi.org/10.1029/2011jb008916

Liu, K., Zhang, J., Yan, X., Liu, Y., Zhang, D., Hu, W. (2016). Safety assessment for inland waterway transportation with an extended fuzzy TOPSIS. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 230 (3), 323–333. https://doi.org/10.1177/1748006x16631869

Liu, J., Jiang, X., Huang, W., He, Y., Yang, Z. (2022). A novel approach for navigational safety evaluation of inland waterway ships under uncertain environment. Transportation Safety and Environment, 4 (1). https://doi.org/10.1093/tse/tdab029

Guidelines and criteria for vessel traffic services on inland waterways: Resolution No. 58 / Working Party on Inland Water Transport, Inland Transport Committee (ECE/TRANS/SC.3/166/Rev.1) (2024). Geneva: UNECE. Available at: https://unece.org/sites/default/files/2024-02/ECE-TRANS-SC.3-166-Rev.1e_0.pdf

Dudchenko, S., Tymochko, O., Makarchuk, D., Golovan, A. (2024). Application of fuzzy cellular automata to optimize a vessel route considering the forecasted hydrometeorological conditions. Eastern-European Journal of Enterprise Technologies, 2 (3 (128)), 28–37. https://doi.org/10.15587/1729-4061.2024.302876

Implementation of River Information Services in Europe (2017). DHI Group. Available at: https://www.dhigroup.com/upload/publications/misc/SurfaceAndGroundwater_SK_CaseStory_Implementation%20of%20river%20information%20services%20in%20Europe.pdf

RIS Index Encoding Guide, Version 3.0 rev.2. (2020). CESNI. Available at: https://ris.cesni.eu/docs/RIS_Index_Encoding_Guide/2019_12_24_RIS_Index_Encoding_Guide_v3p0.rev.2.pdf

Directive 2005/44/EC of the European Parliament and of the Council of 7 September 2005 on harmonised river information services (RIS) on inland waterways in the Community (2005). Official Journal of the European Union, L 255, 152–159. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32005L0044

Market observation report: Inland navigation in Europe – Market observation II/2019 (2019). Central Commission for the Navigation of the Rhine. Strasbourg: CCNR. Available at: https://www.ccr-zkr.org/files/documents/om/om19_II_en.pdf

Commission Regulation (EC) No 414/2007 of 13 March 2007 concerning the technical guidelines for the planning, implementation and operational use of river information services (RIS) referred to in Article 5 of Directive 2005/44/EC of the European Parliament and of the Council on harmonised river information services (RIS) on inland waterways in the Community (2007). Official Journal of the European Union, L 105, 1–34. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32007R0414

Commission Regulation (EC) No 415/2007 of 13 March 2007 concerning the technical specifications for vessel tracking and tracing systems referred to in Article 5 of Directive 2005/44/EC of the European Parliament and of the Council on harmonised river information services (RIS) on inland waterways in the Community (2007). Official Journal of the European Union, L 105, 35–42. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32007R0415

Vessel Tracking and Tracing Standard for Inland Navigation (Edition 1.2) (2013). Strasbourg: Central Commission for the Navigation of the Rhine. Available at: https://www.ccr-zkr.org/files/documents/ris/vtt12_e.pdf

Guidelines and criteria for vessel traffic services on inland waterways (2006). Strasbourg: Central Commission for the Navigation of the Rhine. Available at: https://www.ccr-zkr.org/files/documents/ris/vts_e.pdf

Economic Commission for Europe. (2007). International standard for tracking and tracing on inland waterways (VTT) (Resolution No. 63, ECE/TRANS/SC.3/176). Geneva: United Nations. Available at: https://ris.cesni.eu/docs/File/536/UNECE_Resolution_63.pdf

Golovan, A. I. (2023). Formation of digital strategies for solving problems of increasing the efficiency of cargo ship maintenance systems. Reporter of the Priazovskyi State Technical University. Section: Technical Sciences, 46, 149–158. LOCKSS. https://doi.org/10.31498/2225-6733.46.2023.288184

Liu, W., Shi, C., Liu, Z., Shi, Y. (Eds.) (2025). Maritime Infrastructure for Energy Management and Emission Reduction Using Digital Transformation. Studies in Infrastructure and Control. Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4438-4

Qi, Q., Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585–3593. https://doi.org/10.1109/access.2018.2793265

Huang, L., Pena, B., Liu, Y., Anderlini, E. (2022). Machine learning in sustainable ship design and operation: A review. Ocean Engineering, 266, 112907. https://doi.org/10.1016/j.oceaneng.2022.112907

Abuella, M., Fanaee, H., Nowaczyk, S., Johansson, S., Faghani, E. (2025). Time-series analysis approach for improving energy efficiency of fixed-route passenger vessel in short-sea shipping. Ocean Engineering, 334, 121555. https://doi.org/10.1016/j.oceaneng.2025.121555

Lee, J., Davari, H., Singh, J., Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002

Velasco-Gallego, C., Navas De Maya, B., Matutano Molina, C., Lazakis, I., Cubo Mateo, N. (2023). Recent advancements in data-driven methodologies for the fault diagnosis and prognosis of marine systems: A systematic review. Ocean Engineering, 284, 115277. https://doi.org/10.1016/j.oceaneng.2023.115277

Zhyrov, G. (2020). Analysis of problem optimization of parameters maintenance process according to state with constant periodicity of control. International Journal of Emerging Trends in Engineering Research, 8 (6), 2606–2611. https://doi.org/10.30534/ijeter/2020/63862020

Verma, A. K., Srividya, A., Rana, A., Khattri, S. K. (2012). Optimization of maintenance scheduling of ship borne machinery for improved reliability and reduced cost. International Journal of Reliability, Quality and Safety Engineering, 19 (3), 1250014. https://doi.org/10.1142/s0218539312500143

Haider, R., Kakar, A. M., Khattak, S. B., Rehman, S. U., Maqsood, S., Ullah, M. et al. (2015). Development of optimized maintenance system for vehicle fleet. Journal of Engineering and Applied Sciences, University of Engineering and Technology, Peshawar, 34 (2), 21–28. Available at: https://www.researchgate.net/publication/297136711_DEVELOPMENT_OF_OPTIMIZED_MAINTENANCE_SYSTEM_FOR_VEHICLE_FLEET

Emovon, I., Norman, R. A., Murphy, A. J. (2015). Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29 (3), 519–531. https://doi.org/10.1007/s10845-015-1133-6

Golovan, A., Honcharuk, I., Deli, O., Kostenko, O., Nykyforov, Y. (2021). System of Water Vehicle Power Plant Remote Condition Monitoring. IOP Conference Series: Materials Science and Engineering, 1199 (1), 012049. https://doi.org/10.1088/1757-899x/1199/1/012049

Golovan, A., Gritsuk, I., Rudenko, S., Saravas, V., Shakhov, A., Shumylo, O. (2019). Aspects of Forming the Information V2I Model of the Transport Vessel. 2019 IEEE International Conference on Modern Electrical and Energy Systems (MEES), 390–393. https://doi.org/10.1109/mees.2019.8896595

Kim, T., Song, J. (2018). Generalized Reliability Importance Measure (GRIM) using Gaussian mixture. Reliability Engineering & System Safety, 173, 105–115. https://doi.org/10.1016/j.ress.2018.01.005

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August 19, 2025