The role of information support in the operation of autonomous maritime systems
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Published:
May 4, 2026
Keywords:
autonomous platforms; operation of equipment, maritime transport, navigation, unmanned technologies, information flows; technical condition monitoring; information level; cyber-physical systems; decision support systems.
Main Article Content
Abstract
The article discusses the role of information support in the operation of autonomous offshore platforms as complex cyber-physical systems. It is shown that the effectiveness of monitoring the technical condition of such platforms is determined not only by the availability of sensor data, but primarily by the quality of information flow organisation, its synchronisation and integration. The peculiarities of the formation of navigation, energy, power and environmental data, as well as their temporal and functional heterogeneity, are analysed. The main limitations of classical monitoring systems related to threshold control logic, reactive decision-making, fragmented analysis and high dependence on the operator are identified. The expediency of allocating a separate information level in the structure of autonomous offshore platform operation management, which ensures the conversion of primary asynchronous data into structured information suitable for further analysis and decision support, is substantiated. The results obtained form the conceptual basis for the development of analytical methods for assessing technical condition into operational management systems.
Article Details
How to Cite
Volyansky, S., Melnyk, O., Voloshyn, A., & Dobrovolska, L. (2026). The role of information support in the operation of autonomous maritime systems. Herald of the Odesa National Maritime University, (79), 172-186. https://doi.org/10.47049/2226-1893-2026-1-172-186
Section
Ensuring the safety of navigation
References
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3. Ahmad, A., Li, P., Piechocki, R., & Inacio, R. (2025). Anomaly detection in offshore open radio access network using long short-term memory models on a novel artificial intelligence-driven cloud-native data platform. Engineering Applications of Artificial Intelligence, 161, 112274. https://doi.org/10.1016/j.engappai.2025.112274.
4. Larsen, J.S., Pedersen, S., Liniger, J., & Sørensen, F.F. (2026). Offshore subsea IMR operations: Review of the automation potential. Robotics and Autonomous Systems, 105365. https://doi.org/10.1016/j.robot.2026.105365.
5. Noshchenko, O., Hagspiel, V., & Deshpande, P. C. (2025). Assessing the sustainability of offshore platform power supply alternatives using Multi-Criteria Decision Analysis (MCDA): A case study of Norway. Science of The Total Environment, 973, 179053. https://doi.org/10.1016/j.scitotenv.2025.179053.
6. Wang, Q. (2024). Maritime law enforcement concerning offshore energy platforms: Navigating international law constraints and challenges. Marine Policy, 170, 106370. https://doi.org/10.1016/j.marpol.2024.106370.
7. Li, H., Hao, L., Zhu, Z., Xu, W., Feng, J., & Wei, H. (2025). Real-Time Gas Dispersion Model Prediction on Offshore Platforms based on CNN_ Transformer Model. Process Safety and Environmental Protection, 107983. https://doi.org/10.1016/j.psep.2025.107983.
8. Zhang, M., Tao, L., Nuernberg, M., Rai, A., & Yuan, Z. (2024). Conceptual design of an offshore hydrogen platform. International Journal of Hydrogen Energy, 59, 1004-1013. https://doi.org/10.1016/j.ijhydene.2024.02.077.
9. Brushane, F., Jämsä, K., Lafond, S., & Lilius, J. (2020). A Experimen-tal Research Platform for Maritime Automation and Autonomous Surface Ship Applications. IFAC-PapersOnLine, 54(16), 390-394. https://doi.org/10.1016/j.ifacol.2021.10.121.
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11. Guo, D., Yin, Y., Jing, Q., Shao, Z., & Xu, H. (2026). A LiDAR intensity-enhanced 3D object detection method for maritime autonomous navigation. Ocean Engineering, 343, 123586. https://doi.org/10.1016/j.oceaneng.2025.123586.
12. Othman, M. K., Mohd Sabri, N. S. A., Abdul Rahman, N. S. F., & Osnin, N. A. (2025). Port operators’ perceptions and acceptance of maritime autonomous surface ships (MASS) operations: Insights from Malaysia. Case Studies on Transport Policy, 22, 101567. https://doi.org/10.1016/j.cstp.2025.101567.
13. Li, Z., Mao, Z., Zhang, D., Fan, S., Lyu, W., Zhou, J., & Yang, H. (2025). Mental workload-performance relationships in maritime autonomous surface ship remote control scenarios. Ocean Engineering, 339, 122094. https://doi.org/10.1016/j.oceaneng.2025.122094.
14. Yu, H., Wu, D., Li, G., Lian, T., Li, Y., & Li, F. (2026). Collision avoidance for maritime autonomous surface ship in busy waterways based on the improved deep reinforcement learning and K-means clustering. Ocean Engineering, 343, 123396. https://doi.org/10.1016/j.oceaneng.2025.123396
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16. Belabyad, M., Pyne, R., Paraskevadakis, D., Chang, C., & Kontovas, C. (2025). Technology evolution in maritime autonomous systems: A patent-based analysis. Ocean & Coastal Management, 267, 107744. https://doi.org/10.1016/j.ocecoaman.2025.107744
17. Chymshyr, V. (2023). Conceptual principles of improving the safety and reliability of autonomous technical systems using a ship as an example. Transport Development, (1(16), 79-88. https://doi.org/10.33082/td.2023.1-16.07
18. Melnyk, O., Onyshchenko, O., Voloshyn, A., Vasalatii, N., Lohinov, O., & Koriakin, K. (2022). Development of remote technologies of ship control as a factor for ensuring shipping safety. Transport Development, (3(14), 179-191. https://doi.org/10.33082/td.2022.3-14.13.
19. Reshetkov, D., Bondaryuk, M., & Onyshchenko, S. (2023). Essence, advantages and existing experience of the smart ports development. Transport Development, (4(15), 108-122. https://doi.org/10.33082/td.2022.4-15.09.
20. Simion, D., Postolache, F., Fleacă, B., & Fleacă, E. (2023). AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability. Applied Sciences, 14(20), 9439. https://doi.org/10.3390/app14209439.
21. Sapkota, S., & Paudyal, D.R. (2022). Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region. Sensors, 23(12), 5432. https://doi.org/10.3390/s23125432.