Collision avoidance path planning in multi-ship encounter situations

Abstract

Collision avoidance path planning is still one of the essential problems in the design and application of an intelligent maritime navigation system. Its main obstacle is how to determine effective and cooperative collision avoidance maneuvers within a multi-ship encounter situation. By deconstructing a multi-ship encounter, this study adopted ship domain around target ships to assess the collision danger that own ship should avoid. Subsequently, the fitness function that has multiple dynamic obstacle constraints was designed in a two-dimensional map. Based on DE algorithm, a path-planning method was developed to compute collision-free and optimal navigation paths for ships. Simulation results show that the algorithm can generate a safe and suitable path from each perspective in a multi-ship encounter. The results also validate the practicality of the generated paths, consistency of the algorithm outputs and performance of the algorithm. It would be expected to provide a reference for collision avoidance decision making as well as contribute to the development of autonomous navigation systems.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  1. 1.

    Tam C, Bucknall R, Greig A (2009) Review of collision avoidance and path planning methods for ships in close range encounters. J Navigat 62(3):455–476

    Article  Google Scholar 

  2. 2.

    Huang Y, Chen L, Chen P, Negenborn RR, van Gelder PHAJM (2020) Ship collision avoidance methods: state-of-the-art. Saf Sci 121:451–473

    Article  Google Scholar 

  3. 3.

    Lazarowska A (2017) A new deterministic approach in a decision support system for ship’s trajectory planning. Expert Syst Appl 71:469–478

    Article  Google Scholar 

  4. 4.

    Lyu H, Yin Y (2018) COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields. J Navigat 72(3):588–608

    Article  Google Scholar 

  5. 5.

    Chen C, Chen X-Q, Ma F, Zeng X-J, Wang J (2019) A knowledge-free path planning approach for smart ships based on reinforcement learning. Ocean Eng 189

  6. 6.

    Lazarowska A (2019) A discrete artificial potential field for ship trajectory planning. J Navigat 73(1):233–251

    Article  Google Scholar 

  7. 7.

    Tam C, Bucknall R (2013) Cooperative path planning algorithm for marine surface vessels. Ocean Eng 57:25–33

    Article  Google Scholar 

  8. 8.

    Zhang J, Zhang D, Yan X, Haugen S, Guedes Soares C (2015) A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs. Ocean Eng 105:336–348

    Article  Google Scholar 

  9. 9.

    Tsou M-C (2016) Multi-target collision avoidance route planning under an ECDIS framework. Ocean Eng 121:268–278

    Article  Google Scholar 

  10. 10.

    Li J, Wang H, Guan Z, Pan C (2020) Distributed multi-objective algorithm for preventing multi-ship collisions at sea. J Navigat 73(5):971–990

    Article  Google Scholar 

  11. 11.

    Kang YT, Chen WJ, Zhu DQ, Wang JH, Xie QM (2018) Collision avoidance path planning for ships by particle swarm optimization. J Mar Sci Technol Taiwan 26(6):777–786

    Google Scholar 

  12. 12.

    Tam C, Bucknall R (2010) Collision risk assessment for ships. J Mar Sci Technol 15(3):257–270

    Article  Google Scholar 

  13. 13.

    Storn R, Price K (1997) Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    MathSciNet  Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51279098) and the Scientific Research Project of the Shanghai Science and Technology Committee (Grant No. 18DZ1206104).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yu-Tao Kang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kang, YT., Chen, WJ., Zhu, DQ. et al. Collision avoidance path planning in multi-ship encounter situations. J Mar Sci Technol (2021). https://doi.org/10.1007/s00773-021-00796-z

Download citation

Keywords

  • Collision avoidance
  • Ship domain
  • Path planning
  • Differential evolution algorithm
  • Multi-ship encounter