An UAV-assisted Dynamic Vessel Guidance Route Planning Method for Intelligent Port Navigation Considering Real-time Environmental Evolution

Kai Ma; Wei Pan

doi:10.54691/78fhd005

Authors

Kai Ma Institute for Big Data Research, Liaoning University of International Business and Economics, Dalian 116052, China
Wei Pan Institute for Big Data Research, Liaoning University of International Business and Economics, Dalian 116052, China

DOI:

https://doi.org/10.54691/78fhd005

Keywords:

UAV-assisted Navigation; Intelligent Port; Vessel Guidance; Dynamic Environment Modeling; Collision Risk Assessment; Route Planning.

Abstract

Port traffic changes on a time scale that is poorly served by fixed sensors and precomputed routes. We therefore formulate vessel guidance as a repeatedly updated planning problem and use an unmanned aerial vehicle (UAV) to supply local observations when conventional sources are delayed or incomplete. Electronic navigational chart (ENC) constraints, automatic identification system (AIS) reports, and UAV detections are registered in a time-indexed representation of the navigation area. This representation drives an ECNA-based collision-risk term within the route optimizer, while a feedback loop revises only those route segments affected by new observations. In the simulation case, the resulting route is 0.57% longer than the geometric shortest path but is smoother and remains computable at millisecond scale. Adding UAV observations raises detection coverage from 72.0% to 96.4% and lowers the reported collision-risk index from 0.38 to 0.12. These results indicate that mobile aerial sensing can make port guidance more responsive without sacrificing online computational feasibility.

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References

[1] Molavi, A., Lim, G., & Race, B. (2020). A framework for building a smart port and smart port index. International Journal of Sustainable Transportation, 14, 686–700. https://doi.org/10.1080/15568318.2019.1610919

[2] Kujala, P., Montewka, J., et al. (2017). Towards the assessment of potential impact of unmanned vessels on maritime transportation safety. Reliability Engineering & System Safety.

[3] Murray, B., & Perera, L. P. (2018). A data driven approach to vessel trajectory prediction for safe autonomous ship operations. In 2018 13th International Conference on Industrial and Information Systems (ICDIM). https://doi.org/10.1109/ICDIM.2018.8847003

[4] Porathe, T., Prison, J., & Man, Y. (2014). Situation awareness in remote control centres for unmanned ships.

[5] Yang, D., Wu, L., Wang, S., et al. (2019). How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications. Transport Reviews, 39, 755–773. https://doi.org/10.1080/01441647.2019.1649315

[6] Silveira, P. A. M., Teixeira, A. P., & Soares, C. G. (2013). Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. Journal of Navigation, 66(6), 879–898. https://doi.org/10.1017/S0373463313000519

[7] Lazarowska, A. (2016). A new deterministic approach in a decision support system for ship's trajectory planning. Expert Systems with Applications, 71, 469–478. https://doi.org/10.1016/j.eswa.2016.11.005

[8] Zaccone, R., & Martelli, M. (2019). A collision avoidance algorithm for ship guidance applications. Journal of Marine Engineering & Technology, (1), 1–14. https://doi.org/10.1080/20464177.2019.1685836

[9] Tsou, M. C., & Cheng, H. C. (2013). An ant colony algorithm for efficient ship routing. Polish Maritime Research, 20(3), 28–38. https://doi.org/10.2478/pomr 2013 0032

[10] Szlapczynski, R. (2011). Evolutionary sets of safe ship trajectories: A new approach to collision avoidance. Journal of Navigation, 64(1), 169–181. https://doi.org/10.1017/S0373463310000238

[11] Yahei, F., Kenichi, et al. (1971). Traffic capacity. Journal of Navigation. https://doi.org/10.1017/S0373463300022384

[12] Wang, N. (2013). A novel analytical framework for dynamic quaternion ship domains. Journal of Navigation, 66(2), 265–281. https://doi.org/10.1017/S0373463312000483

[13] Hansen, M. G., Jensen, T. K., Lehn Schjøller, T., et al. (2013). Empirical ship domain based on AIS data. Journal of Navigation, 66(6), 931–940. https://doi.org/10.1017/S0373463313000489

[14] Zhang, W., Goerlandt, F., Montewka, J., et al. (2015). A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107, 60–69. https://doi.org/10.1016/j.oceaneng.2015.07.046

[15] Nomikos, N., Gkonis, P., Bithas, P., et al. (2022). A survey on UAV aided maritime communications: Deployment considerations, applications, and future challenges. IEEE Open Journal of the Communications Society, 4, 56–78. https://doi.org/10.1109/OJCOMS.2022.3225590