Organizational and structural modeling of the integration of marine robotics into multilevel environmental and ecological monitoring systems
Keywords:
marine robotics, autonomous systems, environmental and ecological monitoring, organizational model, adaptability, mathematical modeling, multilevel governance, resource efficiency, ecosystem sustainability, remediationSynopsis
The section explores the basic theoretical and methodological principles of forming an organizational model for using marine robotics (MR) as a tool for implementing systematic and scientifically sound environmental monitoring of the marine environment. Considering new challenges associated with large-scale environmental threats, military operations and increased anthropogenic load, special attention is paid to the development of conceptual approaches that allow for flexibility, sustainability and efficiency of the organizational structure. The focus of the analysis is a combination of a systems approach, engineering cybernetics, ecological systemology and the theory of management of complex socio-technical systems. A list of key principles for building an organizational model is proposed, which covers aspects of goal orientation, adaptability to dynamic external conditions, integration with national and international systems, technological compatibility, security, resource efficiency and transparency. Each of the investigated principles is formalized in the form of a mathematical organizational model, which forms the basis for the subsequent modeling of the functioning of the system in conditions of a dynamically changing environment, including, among other things, scenarios of post-conflict recovery and water resources remediation. Such an approach not only ensures the scientific validity of the construction of the organizational model but also provides tools for practical analysis of the effectiveness of its functioning, support for management decision-making, as well as optimization of interaction between all participants of the environmental monitoring system and risk management. The systematization of the obtained results becomes a key stage in the creation of an integrated management system of robotic platforms for the purposes of environmental and ecological monitoring and water resources remediation. Such a system is capable of scaling, adapting to various tasks and being integrated into the environmental policy of Ukraine, especially in conditions of post-conflict recovery and sustainable development.
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Das, J., Py, F., Harvey, J. B. J., Ryan, J. P., Gellene, A., Graham, R., Caron, D. A. et al. (2015). Data-driven robotic sampling for marine ecosystem monitoring. The International Journal of Robotics Research, 34 (12), 1435–1452. https://doi.org/10.1177/0278364915587723
Rajan, K., Py, F., Barreiro, J. (2012). Towards Deliberative Control in Marine Robotics. Marine Robot Autonomy, 91–175. https://doi.org/10.1007/978-1-4614-5659-9_3
Fiorelli, E., Leonard, N. E., Bhatta, P., Paley, D. A., Bachmayer, R., Fratantoni, D. M. (2006). Multi-AUV Control and Adaptive Sampling in Monterey Bay. IEEE Journal of Oceanic Engineering, 31 (4), 935–948. https://doi.org/10.1109/joe.2006.880429
Blintsov, V. S., Babkin, G. V. (2020). Theoretical basis of design and production of marine robotics. Prospects and priorities of research in science and technology, 25–43. https://doi.org/10.30525/978-9934-26-008-7.1-2
Gordieiev, B. M., Burunina, Zh. Yu., Sabutskyi, I. P. (2024). Information model of an unmanned surface vessel as an object of automation. Methods and instruments of quality control, 2 (53). https://doi.org/10.31471/1993-9981-2024-2(53)-53-59
Nadtochii, V. A., Burunin, A. P. (2024). Automation of payload control of an uncrewed surface boat for research of the marine environment. Methods and devices of quality control, 2 (53). https://doi.org/10.31471/1993-9981-2024-2(53)-69-80
Timchenko, V. L., Nadtochyi, A. V. (2024). Unmanned surface vessel as a component of the automated demining system of Ukraine's internal waters. Visnik of the Volodymyr Dahl East Ukrainian National University, 6 (286), 182–189. https://doi.org/10.33216/1998-7927-2024-286-6-182-188
Zbrutsky, O. V., Sirivchuk, A. S., Klochkov, O. P. (2024). Automation of a robotic marine monitoring network using an unmanned surface vessel. Visnik of the Volodymyr Dahl East Ukrainian National University, 6 (286), 166–173. DOI: https://doi.org/10.33216/1998-7927-2024-286-6-166-173
Kulik, A. S., Nadtochyi, A. V., Burunina, Z. Yu. (2024). Problems of creating mathematical models for researching automatic control systems for mine countermeasure unmanned surface vessel. Vcheni zapysky TNU imeni V.I. Vernadskoho. Seriya: Tekhnichni nauky, 35 (74 (2)), 383–390. https://doi.org/10.32782/2663-5941/2024.2/53
Cherniavskyi, B. (2025). Integration of Drones and Dio-Inspired Algorithms into Intelligent Transportation Logistics Systems for Post-war Remediation of Ukraine. Intelligent Transport Systems: Ecology, Safety, Quality, Comfort, 426–437. https://doi.org/10.1007/978-3-031-87379-9_39
Cherniavska, T., Tanklevska, N., Cherniavskyi, B. (2024). A decision-making system for managing the remediation of water resources in the Kherson region: agent-oriented modeling in the context of post-war economic recovery. Transformations of National Economies under Conditions of Instability, 223–256. https://doi.org/10.21303/978-9916-9850-6-9.ch8
Rogaczewski, R., Cieślak, R., Suszyński, M. (2020). The impact of digitalization and Industry 4.0 on the optimization of production processes and workplace ergonomics. The Malopolska School of Economics in Tarnow Research Papers Collection, 48 (4), 133–145. https://doi.org/10.25944/znmwse.2020.04.133145
Liu, Y., Wang, M., Su, Z., Luo, J., Xie, S., Peng, Y. et al. (2020). Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm. Journal of Marine Science and Engineering, 8 (11), 843. https://doi.org/10.3390/jmse8110843
Yan, T., Xu, Z., Yang, S. X., Gadsden, S. A. (2023). Formation control of multiple autonomous underwater vehicles: a review. Intelligence & Robotics, 3 (1), 1–22. https://doi.org/10.20517/ir.2023.01
Hung, P. D., Vinh, T. Q., Ngo, T. D. (2020). Hierarchical Distributed Control for Global Network Integrity Preservation in Multirobot Systems. IEEE Transactions on Cybernetics, 50 (3), 1278–1291. https://doi.org/10.1109/tcyb.2019.2913326
Seto, M. L., Paull, L., Saeedi, S. (2012). Introduction to Autonomy for Marine Robots. Marine Robot Autonomy, 1–46. https://doi.org/10.1007/978-1-4614-5659-9_1
Moline, M. A., Blackwell, S. M., von Alt, C., Allen, B., Austin, T., Case, J. et al. (2005). Remote Environmental Monitoring Units: An Autonomous Vehicle for Characterizing Coastal Environments. Journal of Atmospheric and Oceanic Technology, 22 (11), 1797–1808. https://doi.org/10.1175/jtech1809.1
Shaw, G. (1990). NOSA (NATO OSI security architecture). IEE Colloquium on Security and Networks. London.
Bensaci, C., Zennir, Y., Pomorski, D., Innal, F., Liu, Y., Tolba, C. (2020). STPA and Bowtie risk analysis study for centralized and hierarchical control architectures comparison. Alexandria Engineering Journal, 59 (5), 3799–3816. https://doi.org/10.1016/j.aej.2020.06.036
Berget, G. E., Fossum, T. O., Johansen, T. A., Eidsvik, J., & Rajan, K. (2018). Adaptive sampling of ocean processes using an AUV with a Gaussian proxy model. IFAC-PapersOnLine, 51(29), 238-243. https://doi.org/10.1016/j.ifacol.2018.09.509.
Nomikos, N., Gkonis, P. K., Bithas, P. S., Trakadas, P. (2022). A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges. arXiv. https://doi.org/10.48550/arXiv.2209.09605
Snigirova, A., Bogatova, Y., Barinova, S. (2021). Assessment of River-Sea Interaction in the Danube Nearshore Area (Ukraine) by Bioindicators and Statistical Mapping. Land, 10 (3), 310. https://doi.org/10.3390/land10030310
Larsen, T. N., Heiberg, A., Meyer, E., Rasheeda, A., San, O., Varagnolo, D. (2021). Risk-based implementation of COLREGs for autonomous surface vehicles. arXiv. https://doi.org/10.48550/arXiv.2112.00115
Skaugset, K., de Sousa, J. B., Sørensen, A. J. (2025). Autonomous robotic organizations for marine operations. Science Robotics, 10 (100). https://doi.org/10.1126/scirobotics.adl2976
Jung, S., Jeong, S., Kang, J., Kang, J. (2023). Marine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing. arXiv. https://doi.org/10.48550/arXiv.2301.03815
Cherniavskyi, B. (2024). Digital technologies as an accelerator of remediation: a strategic vector for the post-war revitalization of ukraine’s territory. Transformational Economy: Theoretical and Practical Aspects. https://doi.org/10.30525/978-9934-26-494-8-29
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September 25, 2025
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Copyright (c) 2025 Tetiana Cherniavska, Viktor Nadtochii, Anatolii Nadtochyi, Dmytro Lomonosov, Robert Cieślak, Bohdan Cherniavskyi
How to Cite
Cherniavska, T., Nadtochii, V., Nadtochyi, A., Lomonosov, D., Cieślak, R., & Cherniavskyi, B. (2025). Organizational and structural modeling of the integration of marine robotics into multilevel environmental and ecological monitoring systems. In T. Cherniavska (Ed.), & T. Cherniavska, A. Onyshchenko, V. Kudin, O. Lytvyn, H. Kashcheieva, V. Samsonkin, I. Bulgakova, O. Yurchenko, R. Mammadova, T. Hasanova, M. Aliyeva, B. Cherniavskyi, V. Petrenko, A. Karnaushenko, K. Melnykova, M. Mammadova, T. Baydyk, Z. Jabrayilova, H. Gasimov, … M. Skarzhynskyi, Ecological systems modeling (pp. 169-195). Scientific Route OÜ®. https://doi.org/10.21303/978-9908-9706-6-0.ch8
