An automatic collision avoidance algorithm for multiple marine surface vehicles

• Autorzy: Hedjar Ramdane , Bounkhel Messaoud

Identyfikatory

DOI

10.2478/amcs-2019-0056

• Języki publikacji: EN

Abstrakty

EN

In recent years, unmanned surface vehicles have been widely used in various applications from military to civil domains. Seaports are crowded and ship accidents have increased. Thus, collision accidents occur frequently mainly due to human errors even though international regulations for preventing collisions at seas (COLREGs) have been established. In this paper, we propose a real-time obstacle avoidance algorithm for multiple autonomous surface vehicles based on constrained convex optimization. The proposed method is simple and fast in its implementation, and the solution converges to the optimal decision. The algorithm is combined with the PD-feedback linearization controller to track the generated path and to reach the target safely. Forces and azimuth angles are efficiently distributed using a control allocation technique. To show the effectiveness of the proposed collision-free path-planning algorithm, numerical simulations are performed.

Słowa kluczowe

EN

unmanned surface vehicle; obstacle avoidance; control allocation; constrained convex optimization

PL

bezzałogowy pojazd nawodny; omijanie przeszkody; układ alokacji; optymalizacja wypukła

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2019
• Tom: Vol. 29, no. 4
• Strony: 759--768
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Hedjar Ramdane

• Department of Computer Engineering, King Saud University, PO Box 5117, Riyadh 11543, Saudi Arabia

autor

Bounkhel Messaoud

• Department of Mathematics, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia

Bibliografia

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• [12] Liu, Y.H. and Shi, C.J. (2005). A fuzzy-neural inference network for ship collision avoidance, Proceedings of the International Conference on Machine Learning and Cybernetics, Guangzhou, China, pp. 4754–4759.
• [13] Mattingley, J. and Boyd, S. (2010). Real-time convex optimization in signal processing: Recent advances that make it easier to design and implement algorithms, IEEE Signal Processing Magazine 27(3): 35–49.
• [14] Perera, L.P. Carvalho, J.P. and Soares, C.G. (2011). Fuzzy logic based decision-making system for collision avoidance of ocean navigation under critical collision conditions, Journal of Marine Science and Technology 6(1): 84–99.
• [15] Skjetne, R. Fossen, T.I. and Kokotovic, P.V. (2005). Adaptive maneuvering, with experiments, for a model ship in marine control laboratory, Automatica 41(2): 289–298.
• [16] Statheros, T. Howells, G. and Maier, K.M. (2008). Autonomous ship collision avoidance navigation concepts, technologies and techniques, The Journal of Navigation 61(1): 129–142.
• [17] Wang, T. F. Yan, X.P. and Wang, Y. (2017). Ship domain model for multi-ship collision avoidance decision making with colregs based on artificial potential field, International Journal on Maritime Navigation and Safety of Sea Transportation 11(1): 85–92.
• [18] Xu, Q. Zhang, C. and Wang, N. (2014). Multi-objective optimization based vessel collision avoidance strategy optimization, Mathematical Problems in Engineering 2014: 1–9.
• [19] Zhang, L. Lin, S., Zhou, J. and Papavassiliou, C. (2017). Three-dimensional underwater path planning based on modified wolf pack algorithm, IEEE Access 5: 22783–22795.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).