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An automatic collision avoidance algorithm for multiple marine surface vehicles

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Warianty tytułu
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.
Rocznik
Strony
759--768
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
  • Department of Computer Engineering, King Saud University, PO Box 5117, Riyadh 11543, Saudi Arabia, hedjar@ksu.edu.sa
  • Department of Mathematics, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia, bounkhel@ksu.edu.sa
Bibliografia
  • [1] Benjamin, M. Curcio, J. and Newman, P. (2006). Navigation of unmanned marine vehicles in accordance with the rules of the road, Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, FL, USA, Vol. 70, pp. 3581–3587.
  • [2] Campbell, S. Naeem, W. and Irwin, G.W. (2002). A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance maneuvers, Annual Reviews in Control 36(2): 267–283.
  • [3] Erol, S. Demir, M.E.B. and Eyüboğlu, E. (2018). Analysis of ship accidents in the Istanbul strait using neuro-fuzzy and genetically optimized fuzzy classifiers, The Journal of Navigation 71(2): 419–436.
  • [4] Fossen, T.I. and Johansen, T.A. (2006). A survey of control allocation methods for ships and underwater vehicles, Proceedings of the 14th Mediterranean Conference on Control and Automation, Ancona, Italy, pp. 1–6.
  • [5] Fossen, T.L. (1994). Guidance and Control of Ocean Vehicles, Wiley, New York, NY.
  • [6] Isidori, A. (1989). Nonlinear Control Systems, Springer, London.
  • [7] Johansen, T.A. and Fossen, T. (2013). Control allocation: A survey, Automatica 49(5): 1087–1103.
  • [8] Kim, D. Hirayama, K. and Okimoto, T. (2017). Distributed stochastic search algorithm for multi-ship encounter situations, The Journal of Navigation 70(4): 699–718.
  • [9] Kuwata, Y., Wolf, M.T.Z.D. and Huntsberger, T.L. (2014). Safe maritime autonomous navigation with COLREGS using velocity obstacles, IEEE Journal of Oceanic Engineering 39(1): 110–119.
  • [10] Kvasov, D.E. and Mukhametzhanov, M. (2018). Metaheuristic vs. deterministic global optimization algorithms: The univariate case, Applied Mathematics and Computation 318(1): 245–259.
  • [11] Lazarowska, A. (2015). Ships trajectory planning for collision avoidance at sea based on ant colony optimization, The Journal of Navigation 68(2): 291–307.
  • [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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-23a70e8b-42c3-4a0d-8afe-da9e29a136b1
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