Modern Method Based on Artificial Intelligence for Safe Control in the Marine Environment

ABSTRACT: The article presents an approach to formulating a ship control process model in order to solve the problem of determining a safe ship trajectory in collision situations. Fuzzy process properties are included in the model to bring it closer to reality, as in many situations the navigator makes a subjective decision. A special neural network was used to solve the presented problem. This artificial neural network is characterized by minimum and maximum operations when set. In order to confirm the correctness of the operation of the proposed algorithm, the results of the simulations obtained were presented and an discussion was conducted.

KEYWORDS: Evolutionary Algorithms, Fuzzy Logic, Collision Avoidance, Safe Ship's Trajectory, Artificial Neural Network (ANN), Dynamic Programming (DP), Artificial Intelligence (AI)

REFERENCES

D. C. Donderi, R. Mercer, M. B. Hong, and D. Skinner, ‘Simulated navigation performance with marine electronic chart and information display systems (ECDIS)’, J. Navig., vol. 57, no. 2, pp. 189–202, May 2004, doi: 10.1017/S0373463304002668. - doi:10.1017/S0373463304002668

A. Weintrit and K. Stawicki, ‘Operational Requirements for Electronic Chart Display and Information Systems (ecdis). Risk of Overreliance on Ecdis’, Transp. Probl., vol. 3, no. 2, pp. 75–82, 2008, Accessed: Apr. 26, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/summary/781e4681-1996-48ac-bba3-e9c24d9d4f0e-85823306/relevance/1

S. Zuskin, D. Brcic, and D. Sabalja, ‘A Contribution to Improving the Standards of ECDIS Training’, Pomor.-Sci. J. Marit. Res., vol. 27, no. 1, pp. 131–148, Jun. 2013, Accessed: Apr. 26, 2023. [Online]. Available: https://www.webofscience.com/wos/woscc/summary/781e4681-1996-48ac-bba3-e9c24d9d4f0e-85823306/relevance/1

T. Abramowicz-Gerigk and J. Jachowski, ‘Ship Berthing and Unberthing Monitoring System in the Ferry Terminal’, Sensors, vol. 22, no. 23, p. 9133, Dec. 2022, doi: 10.3390/s22239133. - doi:10.3390/s22239133

J. Lisowski, ‘Sensitivity of Safe Trajectory in a Game Environment on Inaccuracy of Radar Data in Autonomous Navigation’, Sensors, vol. 19, no. 8, p. 1816, Apr. 2019, doi: 10.3390/s19081816. - doi:10.3390/s19081816

A. Lazarowska, ‘Comparison of discrete artificial potential field algorithm and wave-front algorithm for autonomous ship trajectory planning’, IEEE Access, vol. 8, pp. 221013–221026, 2020. - doi:10.1109/ACCESS.2020.3043539

R. Zaccone and M. Martelli, ‘A random sampling based algorithm for ship path planning with obstacles’, in Proceedings of the International Ship Control Systems Symposium (iSCSS), 2018, p. 4. - doi:10.24868/issn.2631-8741.2018.018

R. Śmierzchalski and A. Witkowska, ‘Advanced Ship Control Methods’, in Automatic Control, Robotics, and Information Processing, Springer, 2021, pp. 617–643. - doi:10.1007/978-3-030-48587-0_20

S. Ni, Z. Liu, Y. Cai, and X. Wang, ‘Modelling of ship’s trajectory planning in collision situations by hybrid genetic algorithm’, Pol. Marit. Res., vol. 25, no. 3 (99), pp. 14–25, 2018. - doi:10.2478/pomr-2018-0092

R. Szlapczynski and H. Ghaemi, ‘Framework of an Evolutionary Multi-Objective Optimisation Method for Planning a Safe Trajectory for a Marine Autonomous Surface Ship’, Pol. Marit. Res., vol. 26, no. 4, pp. 69–79, 2019, doi: 10.2478/pomr-2019-0068. - doi:10.2478/pomr-2019-0068

Z. Pietrzykowski et al., ‘The autonomous navigation system of a sea-going vessel’, Ocean Eng., vol. 261, p. 112104, Oct. 2022, doi: 10.1016/j.oceaneng.2022.112104. - doi:10.1016/j.oceaneng.2022.112104

A. Weintrit and T. Neumann, Advances in Marine Navigation and Safety of Sea Transportation. Introduction. CRC Press, 2019.

K. Janczyk, J. Rumiński, T. Neumann, N. Głowacka, and P. Wiśniewski, ‘Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks’, Appl. Sci., vol. 12, no. 13, Art. no. 13, Jan. 2022, doi: 10.3390/app12136608. - doi:10.3390/app12136608

M. Rybczak and W. Gierusz, ‘Maritime Autonomous Surface Ships in Use with LMI and Overriding Trajectory Controller’, Appl. Sci.-Basel, vol. 12, no. 19, p. 9927, Oct. 2022, doi: 10.3390/app12199927. - doi:10.3390/app12199927

R. Szlapczynski and J. Szlapczynska, ‘A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems’, Reliab. Eng. Syst. Saf., vol. 214, p. 107766, Oct. 2021, doi: 10.1016/j.ress.2021.107766. - doi:10.1016/j.ress.2021.107766

M. Mohamed-Seghir, K. Kula, and A. Kouzou, ‘Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea’, Electronics, vol. 10, no. 19, p. 2360, Oct. 2021, doi: 10.3390/electronics10192360. - doi:10.3390/electronics10192360

J. Lisowski, pham Tiep, and M. Mohamed-Seghir, ‘Safe ship automatic control taking into consideration fuzzy properties of the process’, Pol. Marit. Res., vol. 1, pp. 25–32, Jan. 1994.

P. Borkowski, Z. Pietrzykowski, and J. Magaj, ‘The Algorithm of Determining an Anti-Collision Manoeuvre Trajectory Based on the Interpolation of Ship’s State Vector’, Sensors, vol. 21, no. 16, p. 5332, Aug. 2021, doi: 10.3390/s21165332. - doi:10.3390/s21165332

Y.-Y. Chen, M.-Z. Ellis-Tiew, W.-C. Chen, and C.-Z. Wang, ‘Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels’, Appl. Sci.-Basel, vol. 11, no. 14, p. 6338, Jul. 2021, doi: 10.3390/app11146338. - doi:10.3390/app11146338

R. F. Romero, J. Kacprzyk, and F. Gomide, ‘Neural Network Based Algorithm for Dynamic System Optimization’, Asian J. Control, vol. 3, no. 2, pp. 131–142, 2001, doi: 10.1111/j.1934-6093.2001.tb00052.x. - doi:10.1111/j.1934-6093.2001.tb00052.x

Y. Cho, J. Han, and J. Kim, ‘Efficient COLREG-compliant collision avoidance in multi-ship encounter situations’, IEEE Trans. Intell. Transp. Syst., 2020.

F. Deng, L. Jin, X. Hou, L. Wang, B. Li, and H. Yang, ‘COLREGs: Compliant Dynamic Obstacle Avoidance of USVs Based on the Dynamic Navigation Ship Domain’, J. Mar. Sci. Eng., vol. 9, no. 8, p. 837, Aug. 2021, doi: 10.3390/jmse9080837. - doi:10.3390/jmse9080837

L. Li, D. Wu, Y. Huang, and Z.-M. Yuan, ‘A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field’, Appl. Ocean Res., vol. 113, p. 102759, 2021. - doi:10.1016/j.apor.2021.102759

J. Ning, H. Chen, T. Li, W. Li, and C. Li, ‘COLREGs-Compliant Unmanned Surface Vehicles Collision Avoidance Based on Multi-Objective Genetic Algorithm’, Ieee Access, vol. 8, pp. 190367–190377, 2020, doi: 10.1109/ACCESS.2020.3030262. - doi:10.1109/ACCESS.2020.3030262

Citation note:

Mohamed-Seghir M.: Modern Method Based on Artificial Intelligence for Safe Control in the Marine Environment. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 17, No. 2, doi:10.12716/1001.17.02.03, pp. 283-288, 2023

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