Preview

Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova

Advanced search

Calculating a collision avoidance maneuver for two unmanned ships by minimizing a cost function in MATLAB

https://doi.org/10.21821/2309-5180-2023-15-5-876-884

Abstract

An approach to the problem of collision avoidance of two unmanned vessels in certain area based on cost function minimization is presented in the paper. A script written in the MATLAB computational environment that calculates the optimal maneuver to prevent a collision is described. The cost function in this study is defined as the square of the difference between the safe distance and the Closest Point of Approach, and in order to find the optimal maneuver, it needs to be minimized, for which the fmincon (a MATLAB optimization function) is used in this code. The calculation of collision avoidance maneuvers is made “ from the perspective” of the VTS: it’s optimized for two vessels and allows the vessels to pass each other at a specified distance. The script, taking as input a matrix with data on pairs of approaching vessels (their x and y coordinates, speeds, and courses), by minimizing the cost function, calculates the optimal change in speeds and/or courses for two vessels, allowing them to pass each other at a safe distance. To verify the functionality of the script, a successful simulation is carried out in MATLAB. Several examples of its operation are given. These are situations where the closest point of approach (CPA) is grea-ter than safe one; situations where the CPA is less than safe distance, but the time to CPA is greater than safe time; and situations of dangerous convergence. The calculation results are illustrated with MATLAB graphs. The code of the script described in this paper can be further refined to work in conjunction with other algorithms, and it can also be used to create training datasets for training neural networks to predict safe maneuvers to prevent collisions of unmanned vessels at sea. In this study, the influence of wind and current is not considered, and the COLREGs‑72 is not also taken in consideration. Vessels can maneuver both to port and starboard, as well as reduce speed regardless of the type of approach situation.

About the Author

O. Y. Tripolets
Admiral Makarov State University of Maritime and Inland Shipping
Russian Federation

Tripolets, Oleg Y. - Postgraduate

5/7 Dvinskaya Str., St. Petersburg, 198035



References

1. Chauvin, Christine, Salim Lardjane, Gaël Morel, Jean-Pierre Clostermann, and Benoît Langard. “Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS.” Accident Analysis & Prevention 59 (2013): 26–37. DOI: 10.1016/j.aap.2013.05.006.

2. Zhang, Xinyu, Chengbo Wang, Lingling Jiang, Lanxuan An, and Rui Yang. “Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey.” Ocean Engineering 235 (2021): 109380. DOI: 10.1016/j.oceaneng.2021.109380.

3. Huang, Yamin, Linying Chen, Pengfei Chen, Rudy R. Negenborn, and P.H.A.J.M. van Gelder. “Ship collision avoidance methods: State-of-the-art.” Safety science 121 (2020): 451–473. DOI: 10.1016/j.ssci.2019.09.018.

4. Bertaska, Ivan R., Brual Shah, Karl von Ellenrieder, Petr Švec, Wilhelm Klinger, Armando J. Sinisterra, Manhar Dhanak, and Satyandra K. Gupta. “Experimental evaluation of automatically-generated behaviors for USV operations.” Ocean Engineering 106 (2015): 496–514. DOI: 10.1016/j.oceaneng.2015.07.002.

5. Wright, R. Glenn. “Intelligent autonomous ship navigation using multi-sensor modalities.” Trans- Nav: International Journal on Marine Navigation and Safety of Sea Transportation 13.3 (2019): 503–510. DOI: 10.12716/1001.13.03.03.

6. Li, Yun, and Jian Zheng. “Deep learning structure for collision avoidance planning of unmanned surface vessel.” Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 235.2 (2021): 511–520. DOI: 10.1177/1475090220970102.

7. Praczyk, Tomasz. “Neural anti-collision system for Autonomous Surface Vehicle.” Neurocomputing 149 (2015): 559–572. DOI: 10.1016/j.neucom.2014.08.018.

8. Wang, Chengbo, Xinyu Zhang, Longze Cong, Junjie Li, and Jiawei Zhang. “Research on intelligent collision avoidance decision-making of unmanned ship in unknown environments.” Evolving Systems 10.4 (2019): 649–658. DOI: 10.1007/s12530-018-9253-9.

9. Bukaty, V. M., and S. Y. Morozova. “Direct determination of the close-quarter distance on the basis of importation received from automatic identification systems.” Ekspluatatsiya morskogo transporta 2(68) (2012): 9–15. 10. Cockcroft, A. N., and J.N.F. Lameijer. A Guide to the Collision Avoidance Rules. 7th edition. Butterworth-Heinemann, 2012.

10. Tripolets, O. Yu. “Modelirovanie manevra raskhozhdeniya dvukh sudov v komp’yuternoi srede MATLAB.” Matematicheskie modeli tekhniki, tekhnologii i ekonomiki: materialy Vserossiiskoi studencheskoi nauchno-prakticheskoi konferentsii. SPb.: SPbGLTU, 2023. 16–21.

11. Tripolets, Oleg Y. “Training a neural network to calculate the closest point of approach.” Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova 14.5 (2022): 713–721. DOI: 10.21821/2309-5180-2022-14-5-713-721.


Review

For citations:


Tripolets O.Y. Calculating a collision avoidance maneuver for two unmanned ships by minimizing a cost function in MATLAB. Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova. 2023;15(5):876-884. (In Russ.) https://doi.org/10.21821/2309-5180-2023-15-5-876-884

Views: 228


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2309-5180 (Print)
ISSN 2500-0551 (Online)