DOI QR코드

DOI QR Code

Passage Planning in Coastal Waters for Maritime Autonomous Surface Ships using the D* Algorithm

  • Hyeong-Tak Lee (Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology) ;
  • Hey-Min Choi (Busan BigData Innovation Center, Busan Techno-Park)
  • Received : 2023.04.03
  • Accepted : 2023.05.29
  • Published : 2023.05.31

Abstract

Establishing a ship's passage plan is an essential step before it starts to sail. The research related to the automatic generation of ship passage plans is attracting attention because of the development of maritime autonomous surface ships. In coastal water navigation, the land, islands, and navigation rules need to be considered. From the path planning algorithm's perspective, a ship's passage planning is a global path-planning problem. Because conventional global path-planning methods such as Dijkstra and A* are time-consuming owing to the processes such as environmental modeling, it is difficult to modify a ship's passage plan during a voyage. Therefore, the D* algorithm was used to address these problems. The starting point was near Busan New Port, and the destination was Ulsan Port. The navigable area was designated based on a combination of the ship trajectory data and grid in the target area. The initial path plan generated using the D* algorithm was analyzed with 33 waypoints and a total distance of 113.946 km. The final path plan was simplified using the Douglas-Peucker algorithm. It was analyzed with a total distance of 110.156 km and 10 waypoints. This is approximately 3.05% less than the total distance of the initial passage plan of the ship. This study demonstrated the feasibility of automatically generating a path plan in coastal navigation for maritime autonomous surface ships using the D* algorithm. Using the shortest distance-based path planning algorithm, the ship's fuel consumption and sailing time can be minimized.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (Ministry of Science and ICT) (No. 2022R1C1C2010897).

References

  1. Chen, P., Y. Huang, E. Papadimitriou, J. Mou, and P. van Gelder(2020), Global path planning for autonomous ship: A hybrid approach of Fast Marching Square and velocity obstacles methods. Ocean Engineering, Vol. 214, 107793. 
  2. Douglas, D. H. and T. K. Peucker(1973), Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica, Vol. 10, No. 2, pp. 112-122.  https://doi.org/10.3138/FM57-6770-U75U-7727
  3. Grifoll, M., C. Boren, and M. Castells-Sanabra(2022), A comprehensive ship weather routing system using CMEMS products and A* algorithm. Ocean Engineering, Vol. 255, 111427. 
  4. Jung, Y. H., H. W. Park, S. J. Lee, and M. C. Won(2010), Development of a navigation control algorithm for mobile robots using D* search and fuzzy algorithm. Transactions of the Korean Society of Mechanical Engineers A, Vol. 34, No. 8, pp. 971-980. (in Korean)  https://doi.org/10.3795/KSME-A.2010.34.8.971
  5. Kweon, S. J., S. W. Hwang, S. Lee, and M. J. Jo(2022), Demurrage pattern analysis using logical analysis of data: A case study of the Ulsan Port Authority. Expert Systems with Applications, Vol. 206, 117745. 
  6. Lee, H. T., H. M. Choi, J. S. Lee, H. Yang, and I. S. Cho(2022a), Generation of ship's passage plan using data-driven shortest path algorithms. IEEE Access, Vol. 10, pp. 126217-126231.  https://doi.org/10.1109/ACCESS.2022.3225571
  7. Lee, H. T., J. S. Lee, H. Yang, and I. S. Cho(2021a), An AIS data-driven approach to analyze the pattern of ship trajectories in ports using the DBSCAN algorithm. Applied Sciences, Vol. 11, No. 2, 799. 
  8. Lee, H. T., H. Yang, and I. S. Cho(2021b), Data-driven analysis for safe ship operation in ports using quantile regression based on generalized additive models and deep neural network. Sensors, Vol. 21, No. 24, 8254. 
  9. Lee, H. Y. and S. H. Suh(2000), An implementation of an ENC representation system which meets S-52 presentation specification and S-57 transfer standards. The Journal of the Korea Institute of Maritime Information & Communication Sciences, Vol. 4, No. 2, pp. 469-478. (in Korean) 
  10. Lee, J. S., H. T. Lee, and I. S. Cho(2022b), Maritime traffic route detection framework based on statistical density analysis from AIS data using a clustering algorithm. IEEE Access, Vol. 10, 23355-23366.  https://doi.org/10.1109/ACCESS.2022.3154363
  11. Lee, W., W. Yoo, G. H. Choi, S. H. Ham, and T. W. Kim (2019), Determination of optimal ship route in coastal sea considering sea state and under keel clearance. Journal of the Society of Naval Architects of Korea, Vol. 56, No. 6, pp. 480 -487. (in Korean)  https://doi.org/10.3744/SNAK.2019.56.6.480
  12. Liu, Z., Y. Zhang, X. Yu, and C. Yuan(2016), Unmanned surface vehicles: An overview of developments and challenges. Annual Reviews in Control, Vol. 41, pp. 71-93.  https://doi.org/10.1016/j.arcontrol.2016.04.018
  13. Sang, H., Y. You, X. Sun, Y. Zhou, and F. Liu(2021), The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations. Ocean Engineering, Vol. 223, 108709. 
  14. Silveira, P., A. P. Teixeira, and C. Guedes-Soares(2019), AIS based shipping routes using the Dijkstra algorithm. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 3, pp. 565-571.  https://doi.org/10.12716/1001.13.03.11
  15. Stentz, A.(1997), Optimal and efficient path planning for partially known environments. In: Herbert, M. H., Thorpe, C., and Stentz, A. (eds.), Intelligent Unmanned Ground Vehicles, New York: Springer, pp. 203-220. 
  16. Sun, Y., M. Fang, and Y. Su(2021), AGV path planning based on improved Dijkstra algorithm. Journal of Physics: Conference Series, Vol. 1746, No. 1, 012052. 
  17. Wang, H., W. Mao, and L. Eriksson(2019), A Three-Dimensional Dijkstra's algorithm for multi-objective ship voyage optimization. Ocean Engineering, Vol. 186, 106131. 
  18. Wang, L., Z. Zhang, Q. Zhu, and S. Ma(2020), Ship route planning based on double-cycling genetic algorithm considering ship maneuverability constraint. IEEE Access, Vol. 8, pp. 190746-190759.  https://doi.org/10.1109/ACCESS.2020.3031739
  19. Wee, S. M., S. H. Kim, and I. D. Chang(2000), On the Implementation of Route Planning Algorithms on the Electronic Chart system. Journal of Korea Institute of Navigation, Vol. 24, No. 3, pp. 167-176. (in Korean) 
  20. Yu, J., M. Yang, Z. Zhao, X. Wang, Y. Bai, J. Wu, and J. Xu(2022), Path planning of unmanned surface vessel in an unknown environment based on improved D* Lite algorithm. Ocean Engineering, Vol. 266, 112873. 
  21. Xie, L., S. Xue, J. Zhang, M. Zhang, W. Tian, and S. Haugen(2019), A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters. Ocean Engineering, Vol. 184, pp. 311-322. https://doi.org/10.1016/j.oceaneng.2019.04.055