Hostname: page-component-7dd5485656-wxk4p Total loading time: 0 Render date: 2025-10-30T13:20:19.353Z Has data issue: false hasContentIssue false
  • English
  • Français

A collaborative collision avoidance strategy for autonomous ships under mixed scenarios

Published online by Cambridge University Press:  05 April 2023

Shaobo Wang ,
Yingjun Zhang ,
Feifei Song  and
Wengang Mao
Shaobo Wang
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China Division of Marine Technology, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
Yingjun Zhang*
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Feifei Song
Affiliation:
College of Information Science and Technology, Dalian Maritime University, Dalian, China
Wengang Mao
Affiliation:
Division of Marine Technology, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden
*
*Corresponding author. E-mail: zhangyj@dlmu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Ship collision avoidance has always been one of the classic topics in the field of marine research. In traditional encounter situations, officers on watch (OOWs) usually use a very high frequency (VHF) radio to coordinate each other. In recent years, with the continuous development of autonomous ships, there will be a mixed situation where ships of different levels of autonomy coexist at the same time. Under such a scenario, different decision makers have different perceptions of the current scene and different decision-making logic, so conventional collision avoidance methods may not be applicable. Therefore, this paper proposes a collaborative collision avoidance strategy for multi-ship collision avoidance under mixed scenarios. It builds a multi-ship cooperative network to determine cooperative objects and timing, at the same time. Based on a cooperative game model, a global collision avoidance responsibility distribution that satisfies group rationality and individual rationality is realised, and finally achieves a collaborative strategy according to the generalised reciprocal velocity obstacle (GRVO) algorithm. Case studies show that the strategy proposed in this paper can make all ships pass each other clearly and safely.

Keywords

autonomous shipcollaborative collision avoidancetopological networkcooperative game

Information

Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 2-3 , March 2023 , pp. 200 - 224
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Akdağ, M., Solnør, P. and Johansen, T. A. (2022a). Collaborative collision avoidance for maritime autonomous surface ships: a review. Ocean Engineering, 250, 110920.CrossRefGoogle Scholar
Akdağ, M., Fossen, T. I. and Johansen, T. A. (2022b). Collaborative collision avoidance for autonomous ships using informed scenario-based model predictive control. IFAC-PapersOnLine, 55(31), 249–256.CrossRefGoogle Scholar
Antão, P. and Guedes Soares, C. (2008). Causal factors in accidents of high-speed craft and conventional ocean-going vessels. Reliability Engineering and System Safety, 93, 1292–1304.CrossRefGoogle Scholar
Aylward, K., Weber, R., Lundh, M., MacKinnon, S. N. and Dahlman, J. (2022). Navigators’ views of a collision avoidance decision support system for maritime navigation. The Journal of Navigation, 75(5), 1035–1048.CrossRefGoogle Scholar
Bakdi, A., Glad, I. K. and Vanem, E. (2021). Testbed scenario design exploiting traffic big data for autonomous ship trials under multiple conflicts with collision/grounding risks and spatio-temporal dependencies. IEEE Transactions on Intelligent Transportation Systems, 22(12), 7914–7930.CrossRefGoogle Scholar
Calvert, E. S. (1997). Manoeuvres to ensure the avoidance of collision. The Journal of Navigation, 50(3), 400–410.CrossRefGoogle Scholar
Chen, P., Huang, Y., Mou, J. and Van Gelder, P. H. A. J. M. (2019). Probabilistic risk analysis for ship-ship collision: state-of-the-art. Safety Science, 117, 108–122.CrossRefGoogle Scholar
Huang, Y., Chen, L., Chen, P., Negenborn, R. R. and Van Gelder, P. H. A. J. M. (2020). Ship collision avoidance methods: state-of-the-art. Safety Science, 121, 451–473.CrossRefGoogle Scholar
International Maritime Organization (IMO). (1972). [with amendments adopted from December 2009], Convention on the international regulations for preventing collisions at sea.Google Scholar
Johansen, T. A., Perez, T. and Cristofaro, A. (2016). Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Transactions on Intelligent Transportation Systems, 17(12), 3407–3422.CrossRefGoogle Scholar
Kim, D., Hirayama, K. and Okimoto, M. (2015). Ship collision avoidance by distributed tabu search. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 9(1), 23–29.CrossRefGoogle Scholar
Kuwata, Y., Wolf, M. T., Zarzhitsky, D. and Huntsberger, T. L. (2013). Safe maritime autonomous navigation with COLREGS, using velocity obstacles. IEEE Journal of Oceanic Engineering, 39(1), 110–119.CrossRefGoogle Scholar
Li, S., Liu, J. and Negenborn, R. R. (2019a). Distributed coordination for collision avoidance of multiple ships considering ship maneuverability. Ocean Engineering, 181, 212–226.CrossRefGoogle Scholar
Li, S., Liu, J., Negenborn, R. R. and Ma, F. (2019b). Optimizing the joint collision avoidance operations of multiple ships from an overall perspective. Ocean Engineering, 191, 106511.CrossRefGoogle Scholar
Li, J., Wang, H., Guan, Z. and Pan, C. (2020). Distributed multi-objective algorithm for preventing multi-ship collisions at sea. The Journal of Navigation, 73(5), 971–990.CrossRefGoogle Scholar
Lisowski, J. (2012). Game control methods in avoidance of ships collisions. Polish Maritime Research, 19(S1 (74)), 3–10.CrossRefGoogle Scholar
Liu, H., Deng, R. and Zhang, L. (2016). The Application Research for Ship Collision Avoidance with Hybrid Optimization Algorithm. In 2016 IEEE International Conference on Information and Automation (ICIA), 760–767CrossRefGoogle Scholar
Lušić, Z., Bakota, M., Čorić, M. and Skoko, I. (2019). Seafarer market–challenges for the future. Transactions on Maritime Science, 8(01), 62–74.CrossRefGoogle Scholar
Lyu, H. and Yin, Y. (2019). COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields. The Journal of Navigation, 72(3), 588–608.CrossRefGoogle Scholar
Pedersen, T. A., Glomsrud, J. A., Ruud, E. L., Simonsen, A., Sandrib, J. and Eriksen, B. O. H. (2020). Towards simulation-based verification of autonomous navigation systems. Safety Science, 129, 104799.CrossRefGoogle Scholar
Shaobo, W., Yingjun, Z. and Lianbo, L. (2020). A collision avoidance decision-making system for autonomous ship based on modified velocity obstacle method. Ocean Engineering, 215, 107910.CrossRefGoogle Scholar
Shapley, L. S. and Shubik, M. (1954). A method for evaluating the distribution of power in a committee system. American Political Science Review, 48(3), 787–792.CrossRefGoogle Scholar
Shen, H., Hashimoto, H., Matsuda, A., Taniguchi, Y., Terada, D. and Guo, C. (2019). Automatic collision avoidance of multiple ships based on deep Q-learning. Applied Ocean Research, 86, 268–288.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2012). On evolutionary computing in multi-ship trajectory planning. Applied Intelligence, 37(2), 155–174.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2016). An analysis of domain-based ship collision risk parameters. Ocean Engineering, 126, 47–56.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2017). Review of ship safety domains: Models and applications. Ocean Engineering, 145, 277–289.CrossRefGoogle Scholar
Tam, C. and Bucknall, R. (2013). Cooperative path planning algorithm for marine surface vessels. Ocean Engineering, 57, 25–33.CrossRefGoogle Scholar
Tam, C., Bucknall, R. and Greig, A. (2009). Review of collision avoidance and path planning methods for ships in close range encounters. The Journal of Navigation, 62(3), 455–476.CrossRefGoogle Scholar
Van den Berg, J., Lin, M. and Manocha, D. (2008). Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation. In 2008 IEEE International Conference on Robotics and Automation (ICRA), 1928–1935.CrossRefGoogle Scholar
Ventikos, N. P., Chmurski, A. and Louzis, K. (2020). A systems-based application for autonomous vessels safety: Hazard identification as a function of increasing autonomy levels. Safety Science, 131, 104919.CrossRefGoogle Scholar
Wang, T., Wu, Q., Zhang, J., Wu, B. and Wang, Y. (2020). Autonomous decision-making scheme for multi-ship collision avoidance with iterative observation and inference. Ocean Engineering, 197, 106873.CrossRefGoogle Scholar
Wang, S., Zhang, Y. and Zheng, Y. (2021). Multi-ship encounter situation adaptive understanding by individual navigation intention inference. Ocean Engineering, 237, 109612.CrossRefGoogle Scholar
Wilthil, E. F., Flåten, A. L., Brekke, E. F. and Breivik, M. (2018). Radar-based Maritime Collision Avoidance Using Dynamic Window. In 2018 IEEE Aerospace Conference, 1–9.Google Scholar
Xie, S., Garofano, V., Chu, X. and Negenborn, R. R. (2019). Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks. Ocean Engineering, 193, 106609.CrossRefGoogle Scholar
Xue, Y., Clelland, D., Lee, B. S. and Han, D. (2011). Automatic simulation of ship navigation. Ocean Engineering, 38(17-18), 2290–2305.CrossRefGoogle Scholar
Zhang, J., Zhang, D., Yan, X., Haugen, S. and Soares, C. G. (2015). A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs. Ocean Engineering, 105, 336–348.CrossRefGoogle Scholar
Zhang, M., Montewka, J., Manderbacka, T., Kujala, P. and Hirdaris, S. (2021a). A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditions. Reliability Engineering & System Safety, 213, 107674.CrossRefGoogle Scholar
Zhang, M., Conti, F., Le Sourne, H., Vassalos, D., Kujala, P., Lindroth, D. and Hirdaris, S. (2021b). A method for the direct assessment of ship collision damage and flooding risk in real conditions. Ocean Engineering, 237, 109605.CrossRefGoogle Scholar
Zhang, M., Zhang, D., Fu, S., Kujala, P. and Hirdaris, S. (2022a). A predictive analytics method for maritime traffic flow complexity estimation in inland waterways. Reliability Engineering & System Safety, 220, 108317.CrossRefGoogle Scholar
Zhang, M., Kujala, P. and Hirdaris, S. (2022b). A machine learning method for the evaluation of ship grounding risk in real operational conditions. Reliability Engineering & System Safety, 226, 108697.CrossRefGoogle Scholar
Zhang, J., Liu, J., Hirdaris, S., Zhang, M. and Tian, W. (2023). An interpretable knowledge-based decision support method for ship collision avoidance using AIS data. Reliability Engineering & System Safety, 230, 108919.CrossRefGoogle Scholar
Zhao, L. and Roh, M. I. (2019). COLREGs-compliant multiship collision avoidance based on deep reinforcement learning. Ocean Engineering, 191, 106436.CrossRefGoogle Scholar
Zhao, Y., Li, W. and Shi, P. (2016). A real-time collision avoidance learning system for unmanned surface vessels. Neurocomputing, 182, 255–266.CrossRefGoogle Scholar