Weather Routing System Architecture Using Onboard Data Collection and Route Optimisation

Szłapczyńska, Joanna; Vettor, Roberto; Szłapczyński, Rafał; Łącki, Mirosław; Życzkowski, Marcin; Hinostroza, Miguel A.; Santos, Fernando P.; Tycholiz, Wojciech; Soares, C. Guedes

doi:10.2478/pomr-2022-0020

Abstract

This paper describes the architecture of a weather routing system consisting of two key elements: onboard monitoring and route optimiser sub-systems. The former is responsible for collecting various onboard measurements, such as current ship position or ship motion variables. These data, when gathered and processed, are then used for fine-tuning a ship model. The model, together with weather forecasts, is utilised by a multi-objective route optimiser to estimate forecasted ship responses during the voyage. The route optimiser has been developed in a client-server architecture to reallocate all necessary high-tech resources to the server side and keep the client software as simple and light as possible. The system also includes a module responsible for optimising transmission costs, to reduce onboard transmission during the voyage. The entire solution has been deployed onboard the demonstrator ship ‘Monte da Guia’ and tested during its operations at sea.

References

1. R. W. James, Application of wave forecast to marine navigation. US Naval Oceanograph, 1957.
Search in Google Scholar Back to article
2. H. Hagiwara and J. A. Spaans, “Practical Weather Routing of Sail-assisted Motor Vessels,” J. Navig., vol. 40, no. 01, pp. 96–119, Jan. 1987, doi: 10.1017/S0373463300000333.
Open DOI Search in Google Scholar Back to article
3. C. Chen, S. Shiotani, and K. Sasa, “Numerical ship navigation based on weather and ocean simulation,” Ocean Eng., vol. 69, pp. 44–53, 2013, doi: 10.1016/j.oceaneng.2013.05.019.
Open DOI Search in Google Scholar Back to article
4. W. Shao, P. Zhou, and S. K. Thong, “Development of a novel forward dynamic programming method for weather routing,” J. Mar. Sci. Technol., vol. 17, no. 2, pp. 239–251, 2012, doi: 10.1007/s00773-011-0152-z.
Open DOI Search in Google Scholar Back to article
5. R. Zaccone, E. Ottaviani, M. Figari, and M. Altosole, “Ship voyage optimisation for safe and energy-efficient navigation: A dynamic programming approach,” Ocean Eng., vol. 153, pp. 215–224, 2018, doi: 10.1016/j.oceaneng.2018.01.100.
Open DOI Search in Google Scholar Back to article
6. G. Mannarini, G. Coppini, P. Oddo, and N. Pinardi, “A Prototype of Ship Routing Decision Support System for an Operational Oceanographic Service,” TransNav, Int. J. Mar. Navig. Saf. Sea Transp., vol. 7, no. 1, pp. 53–59, 2013, doi: 10.12716/1001.07.01.06.
Open DOI Search in Google Scholar Back to article
7. G. Mannarini, L. Carelli, J. Orović, C. P. Martinkus, and G. Coppini, “Towards least-CO2 ferry routes in the Adriatic sea,” J. Mar. Sci. Eng., vol. 9, no. 2, pp. 1–15, 2021, doi: 10.3390/jmse9020115.
Open DOI Search in Google Scholar Back to article
8. M. Zyczkowski, P. Krata, and R. Szłapczyński, “Multi-objective weather routing of sailboats considering wave resistance,” Polish Marit. Res., vol. 25, no. 1, pp. 4–12, 2018, doi: 10.2478/pomr-2018-0001.
Open DOI Search in Google Scholar Back to article
9. M. Zyczkowski and R. Szłapczyński, “Multi-Objective Weather Routing of Sailing Vessels,” Polish Marit. Res., vol. 24, no. 4, 2017, doi: 10.1515/pomr-2017-0130.
Open DOI Search in Google Scholar Back to article
10. M.-C. Tsou, “Integration of a Geographic Information System and Evolutionary Computation for Automatic Routing in Coastal Navigation,” J. Navig., vol. 63, no. 02, pp. 323–341, 2010, doi: 10.1017/S0373463309990385.
Open DOI Search in Google Scholar Back to article
11. L. Skoglund, J. Kuttenkeuler, and A. Rosén, “A new method for robust route optimisation in ensemble weather forecasts,” 2012.
Search in Google Scholar Back to article
12. J. Hinnenthal and G. Clauss, “Robust Pareto-optimum routing of ships utilising deterministic and ensemble weather forecasts,” Ships Offshore Struct., vol. 5, no. 2, pp. 105–114, 2010, doi: 10.1080/17445300903210988.
Open DOI Search in Google Scholar Back to article
13. J. Szłapczyńska, “Multi-objective Weather Routing with Customised Criteria and Constraints,” J. Navig., vol. 68, pp. 338–354, 2015, doi: 10.1017/S0373463314000691.
Open DOI Search in Google Scholar Back to article
14. R. Vettor and C. Guedes Soares, “Development of a ship weather routing system,” Ocean Eng., vol. 123, no. January 2018, pp. 1–14, 2016, doi: 10.1016/j.oceaneng.2016.06.035.
Open DOI Search in Google Scholar Back to article
15. R. Vettor, J. Szlapczynska, R. Szlapczynski, W. Tycholiz, and C. Guedes Soares, “Towards Improving Optimised Ship Weather Routing,” Polish Marit. Res., vol. 27, no. 1, pp. 60–69, Mar. 2020, doi: 10.2478/pomr-2020-0007.
Open DOI Search in Google Scholar Back to article
16. W. Kazimierski and N. Wawrzyniak, “Exchange of navigational information between VTS and RIS for inland shipping user needs,” Commun. Comput. Inf. Sci., vol. 471, pp. 294–303, 2014, doi: 10.1007/978-3-662-45317-9_31.
Open DOI Search in Google Scholar Back to article
17. Y. Zhang, A. Zhang, D. Zhang, Z. Kang, and Y. Liang, “Design and Development of Maritime Data Security Management Platform,” Appl. Sci., vol. 12, no. 2, 2022, doi: 10.3390/app12020800.
Open DOI Search in Google Scholar Back to article
18. M. A. Hinostroza and C. Guedes Soares, “Parametric estimation of the directional wave spectrum from ship motions,” Trans. R. Inst. Nav. Archit. Part A Int. J. Marit. Eng., vol. 158, no. December, pp. A121–A130, 2016, doi: 10.3940/rina.2016.a2.356.
Open DOI Search in Google Scholar Back to article
19. Davis, “Wireless Vantage Pro2 & Vantage Pro2 Plus Stations (6152,6153,6162,6163),” vol. 2. 2010, [Online]. Available: http://www.weathershop.com/Specs/6152-62-53-63_VP2Spec_C.pdf.
Search in Google Scholar Back to article
20. Davis, “Integrated Sensor Suite.” [Online]. Available:https://cdn.shopify.com/s/files/1/0515/5992/3873/files/07395-333_IM-6322C-6334.pdf.
Search in Google Scholar Back to article
21. Davis, “WEATHER LINK Software User’s Guide.” [Online]. Available: http://www.davis-tr.com/Downloads/WeatherLink_for_Windows_4.0_7862_Instruction_Manual.pdf.
Search in Google Scholar Back to article
22. Katronic, “KATflow 100 Standard Clamp-On Ultrasonic Flow Transmitter,” 2018. http://www.em-solutions.co.uk/upload/product/pdf/1420207860katflow 100 cost effective clamp-on flow meter.pdf.
Search in Google Scholar Back to article
23. Four-Faith, “Four-Faith. F8L10T – LoRa Terminal.” pp. 10–12, [Online]. Available: https://en.four-faith.com/uploadfile/2018/0913/20180913113549104.pdf.
Search in Google Scholar Back to article
24. Four-Faith, “F8936-L Series Router User Manual.” https://en.four-faith.com/uploadfile/2017/0309/20170309105843459.pdf.
Search in Google Scholar Back to article
25. RockTest, “Vibrating Wire Surface Strain Gauge (Model SM-5A), RockTest Instruction Manual,” 2005, [Online]. Available: https://smartec.ch/wp-content/uploads/sites/2/2017/01/SM-5-Manual.pdf.
Search in Google Scholar Back to article
26. RockTest, “RT-VLOG - VW Datalogger,” [Online]. Available: https://roctest.com/wp-content/uploads/2017/01/E50374-160815-RT-VLOG.pdf.
Search in Google Scholar Back to article
27. RockTest, “RT-VLOG, Instruction Manual.” 2009, [Online]. Available: https://roctest.com/wp-content/uploads/2017/01/RT-VLOG_E1374-180514-1.pdf.
Search in Google Scholar Back to article
28. Q. Zhang and H. Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition,” IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712–731, Dec. 2007, doi: 10.1109/TEVC.2007.892759.
Open DOI Search in Google Scholar Back to article
29. R. Szlapczynski and J. Szlapczynska, “W-dominance: Tradeoff-inspired dominance relation for preference-based evolutionary multi-objective optimisation,” Swarm Evol. Comput., vol. 63, no. March 2020, p. 100866, 2021, doi: 10.1016/j.swevo.2021.100866.
Open DOI Search in Google Scholar Back to article
30. P. Krata, A. Kniat, R. Vettor, H. Krata, and C. Guedes Soares, “The Development of a Combined Method to Quickly Assess Ship Speed and Fuel Consumption at Different Powertrain Load and Sea Conditions,” TransNav, Int. J. Mar. Navig. Saf. Sea Transp., vol. 15, no. 2, pp. 437–444, 2021, doi: 10.12716/1001.15.02.23.
Open DOI Search in Google Scholar Back to article
31. L. Moreira, R. Vettor, and C. Guedes Soares, “Neural network approach for predicting ship speed and fuel consumption,” J. Mar. Sci. Eng., vol. 9, no. 2, pp. 1–14, 2021, doi: 10.3390/jmse9020119.
Open DOI Search in Google Scholar Back to article

Weather Routing System Architecture Using Onboard Data Collection and Route Optimisation

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