TPS-Genetic Algorithm for Real-Time Sailing Route Planning based on Potential Field Theory



Abstract Views
146

Downloads
137

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted May 19, 2023
Published Jun 28, 2023
10.24018/ejeng.2023.8.3.3061

Abstract viewed = 146 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Real-time route planning is always a difficulty in maritime traffic. Route planning must take into account the complex meteorological environment. Route planning based on meteorological environment real-time update is the basis of route feasibility. Based on potential field theory, this paper proposes a TPS - Genetic algorithm for real-time route planning of sailing ships. On the basis of genetic algorithm, combined with the characteristics of route planning, the turning point sorting operation is added to improve the calculation efficiency, and further improve the real-time performance of route planning. Simulation experiments are established and compared with A* algorithm. The experimental results show that the potential field theory can accurately express the dynamic changes of Marine meteorology, and the path planned by TPS - Genetic algorithm is more suitable for real-time navigation environment. TPS - Genetic algorithm can be applied to ship navigation system, which can further adjust the potential energy base and plan routes according to the needs of shipping companies.
Keywords: Genetic Algorithm (GA) Potential Energy Route Planning Sailing Ship

Downloads

Download data is not yet available.

References

  1. Xie W, Fang X, Sheng Nan Wu. 2.5D Navigation Graph and Improved A-Star Algorithm for Path Planning in Ship inside Virtual Environment. 2020 May 1.
    DOI  |   Google Scholar
  2. Li D, Wang P, Du L. Path Planning Technologies for Autonomous Underwater Vehicles-A Review. IEEE Access [Internet]. 2019 [cited 2022 Mar 17];7:9745–68. Available from: https://ieeexplore.ieee.org/document/8581415.
    DOI  |   Google Scholar
  3. Cai J, Chen G, Lützen M, Rytter NGM. A practical AIS-based route library for voyage planning at the pre-fixture stage. Ocean Engineering. 2021 Sep;236:109478.
    DOI  |   Google Scholar
  4. Yan Z, Xiao Y, Cheng L, He R, Ruan X, Zhou X, et al. Exploring AIS data for intelligent maritime routes extraction. Applied Ocean Research. 2020 Aug;101:102271.
    DOI  |   Google Scholar
  5. Koyama T, Nakanowatari T, Inoue J. Information retrieval for Northern Sea Route (NSR) navigation: A statistical approach using the AIS and TOPAZ4 data. Polar Science. 2021 Mar;27:100626.
    DOI  |   Google Scholar
  6. Zhang S, Shi G, Liu Z, Zhao Z, Wu Z. Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity. Ocean Engineering. 2018 May;155:240–50.
    DOI  |   Google Scholar
  7. Wen Y, Sui Z, Zhou C, Xiao C, Chen Qianqian, Dong Soo Han, et al. Automatic ship route design between two ports: A data-driven method. 2020 Mar 1;96:102049–9.
    DOI  |   Google Scholar
  8. Zhang D, Zhang Y, Zhang C. Data mining approach for automatic ship-route design for coastal seas using AIS trajectory clustering analysis. Ocean Engineering [Internet]. 2021 Sep 15 [cited 2022 Dec 18];236:109535. Available from: https://www.sciencedirect.com/science/article/pii/S0029801821009288.
    DOI  |   Google Scholar
  9. Xie L, Xue S, Zhang J, Zhang M, Tian W, Haugen S. A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters. Ocean Engineering [Internet]. 2019 Jul 15 [cited 2022 Nov 23];184:311–22. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0029801818318365.
    DOI  |   Google Scholar
  10. Liang C, Zhang X, Han X. Route planning and track keeping control for ships based on the leader-vertex ant colony and nonlinear feedback algorithms. 2020 Aug 1;101:102239–9.
    DOI  |   Google Scholar
  11. Tsou MC, Kao SL, Su CM. Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts. Journal of Navigation. 2009 Dec 1;63(1):167–82.
    DOI  |   Google Scholar
  12. Mohammed MA, Abd Ghani MK, Hamed RI, Mostafa SA, Ahmad MS, Ibrahim DA. Solving vehicle routing problem by using improved genetic algorithm for optimal solution. Journal of Computational Science [Internet]. 2017 Jul [cited 2019 Dec 1];21:255–62. Available from: https://www.sciencedirect.com/science/article/pii/S1877750317303848.
    DOI  |   Google Scholar
  13. Pan C, Zhang Z, Sun W, Shi J, Wang H. Development of ship weather routing system with higher accuracy using SPSS and an improved genetic algorithm. Journal of Marine Science and Technology. 2021 Feb 19;26(4):1324–39.
    DOI  |   Google Scholar
  14. Adland R, Jia H, Lode T, Skontorp J. The value of meteorological data in marine risk assessment. Reliability Engineering & System Safety [Internet]. 2021 May 1 [cited 2022 Aug 22];209:107480. Available from: https://www.sciencedirect.com/science/article/pii/S0951832021000466.
    DOI  |   Google Scholar
  15. Shah BC, Gupta SK. Long-Distance Path Planning for Unmanned Surface Vehicles in Complex Marine Environment. 2020 Jul 1;45(3):813–30.
    DOI  |   Google Scholar
  16. Zhang M, Montewka J, Manderbacka T, Kujala P, Hirdaris S. A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions. Reliability Engineering & System Safety. 2021 Sep;213:107674.
    DOI  |   Google Scholar
  17. Sen D, Padhy CP. An approach for development of a ship routing algorithm for application in the North Indian Ocean region. Applied Ocean Research. 2015 Mar;50:173–91.
    DOI  |   Google Scholar
  18. Ma W, Ma D, Ma Y, Zhang J, Wang D. Green maritime: a routing and speed multi-objective optimization strategy. Journal of Cleaner Production. 2021 Jul;305:127179.
    DOI  |   Google Scholar
  19. Zhang Y, Yuan C. Research on the synergy of solar-sail ship with route optimization, 2015 International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 2015, pp. 447-450, doi: 10.1109/ICTIS.2015.7232081.
    DOI  |   Google Scholar
  20. Lee H, Park MY, Park S, Rhee SH. Prediction of velocity and attitude of a yacht sailing upwind by computational fluid dynamics. International Journal of Naval Architecture and Ocean Engineering. 2016 Jan;8(1):1–12.
    DOI  |   Google Scholar
  21. Langbein J, Stelzer R, Thom Frühwirth. A Rule-Based Approach to Long-Term Routing for Autonomous Sailboats. 2011 Jan 1;195–204.
    DOI  |   Google Scholar
  22. Ni D, Wang H. A unified perspective on traffic flow theory. Part III: Validation and benchmarking. Applied Mathematical Sciences. 2013;7:1965–82.
    DOI  |   Google Scholar
  23. Ni D. A unified perspective on traffic flow theory. Part II: The unified diagram. Applied Mathematical Sciences. 2013;7:1947–63.
    DOI  |   Google Scholar
  24. Ni D. A unified perspective on traffic flow theory. Part I: The field theory. Applied Mathematical Sciences. 2013;7:1929–46.
    DOI  |   Google Scholar
  25. Li L, Gan J, Zhou K, Qu X, Ran B. A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory. Physica A: Statistical Mechanics and its Applications [Internet]. 2020 Dec 1 [cited 2021 Dec 1];559:125039. Available from: https://www.sciencedirect.com/science/article/pii/S0378437120305410#b19
    DOI  |   Google Scholar
  26. Liu YH, Wang T, Xu H. PE-A* Algorithm for Ship Route Planning Based on Field Theory. 2022 Jan 1;10:36490–504.
    DOI  |   Google Scholar
  27. Henderson N, Pena L. The inverse distance weighted interpolation applied to a particular form of the path tubes Method: Theory and computation for advection in incompressible flow. Applied Mathematics and Computation. 2017 Jul;304:114–35.
    DOI  |   Google Scholar
  28. Amoroso CL, Liverani A, Caligiana G. Numerical investigation on optimum trim envelope curve for high performance sailing yacht hulls. Ocean Engineering. 2018 Sep;163:76–84.
    DOI  |   Google Scholar
  29. Day AH. Performance prediction for sailing dinghies. Ocean Engineering. 2017 May;136:67–79.
    DOI  |   Google Scholar
  30. Yu Y, Zhu H, Yang L, Wang CX. Spatial Indexing for Effective Visualization of Vector-Based Electronic Nautical Chart. 2016 Dec 1.
    DOI  |   Google Scholar