Chapter 7. Analysis of modern underwater navigation and design capabilities of underwater cargo vessels

Authors

Anastasiia Zaiets
Odesa National Maritime University
https://orcid.org/0000-0002-5803-9069
Keywords: underwater space, autonomous underwater vehicle, hydroacoustic systems, navigation, cargo transportation, behavioral architecture, hierarchical architecture

Synopsis

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

The aim of the study is to analyze the advantages and disadvantages of modern navigation methods for autonomous underwater vehicles and their groups, including the use of neural networks, and to determine their development prospects; as well as to enhance the effectiveness of deep-sea surveying and the execution of various underwater operations through the use of advanced mathematical support for autonomous underwater vehicles; the development of underwater space in the interests of maritime freight transport as such, which increases the carrying capacity of existing sea transport routes, increases energy efficiency and reduces the risks of freight transport, provided there is no negative impact on the movement of the vehicle by wind, surface waves and drift currents.

The challenges of developing a control system for autonomous underwater vehicles have been examined. A new architecture of mathematical support for the control system of autonomous underwater vehicles is proposed, which incorporates both hierarchical and behavior-based control structures. This significantly expands the capabilities of these vehicles, enabling them to solve tasks of various classes under the constraints of onboard computational network resources.

Within the proposed architecture, a behavior-based approach is applied at different levels of the functional hierarchical control system. A methodology is substantiated for constructing a tactical-level agent library based on the functional decomposition of the target task class. The structure of an agent has been developed and studied; it includes a local environmental model, tools for planning actions based on this model, and mechanisms for analyzing the utilized information to assess the agent’s operational effectiveness.

Examined the development of underwater space in the interests of maritime freight transport as such, which increases the carrying capacity of existing sea transport routes, increases energy efficiency and reduces the risks of freight transport, provided there is no negative impact on the movement of the vehicle by wind, surface waves and drift currents. It is proposed to create an extensive system of cargo transportation in the underwater space as an alternative to conventional shipping. It is established that underwater data transmission based on lasers and radio waves is effective for data transmission only in conditions where the underwater transport vessel moves in the near-surface layer of the ocean.

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Author Biography

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

References

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

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