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Simulation and application of model-based and model-free track controllers for ship manoeuvring in shallow and confined water Eloot, K.; Cisneros, M.; Donatini, L.; Mansuy, M.; Daems, W.; Massobrio, R.; Hellinckx, P. (2025). Simulation and application of model-based and model-free track controllers for ship manoeuvring in shallow and confined water, in: Eloot, K. et al. 7th MASHCON International Conference on Ship Manoeuvring in Shallow and Confined Water with non-exclusive focus on clean power in shallow water, 18-21 May 2025, Bruges, Belgium: conference program and proceedings. pp. 77-95. https://dx.doi.org/10.48607/331 In: Eloot, K. et al. (2025). 7th MASHCON International Conference on Ship Manoeuvring in Shallow and Confined Water with non-exclusive focus on clean power in shallow water, 18-21 May 2025, Bruges, Belgium: conference program and proceedings. Flanders Hydraulics/Ghent University: Antwerp. XII, 529 pp. https://dx.doi.org/10.48607/316, meer

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Beschikbaar in	Auteurs
Waterbouwkundig Laboratorium: Open Repository 414246 [ OWA ]
Documenttype: Congresbijdrage

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Trefwoorden

Harbours and waterways > Manoeuvring behaviour > Autonomous navigation Simulations

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Auteurs		Top
Eloot, K., meer Cisneros, M. Donatini, L. Mansuy, M.	Daems, W. Massobrio, R. Hellinckx, P.

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Abstract

If autonomous navigation in dense harbours and waterways will be made possible, knowledge on different domains will have to be incorporated. The paper presents a short background on model-based and model-free track controllers that are used for systematic research on the accessibility of ships in shallow and confined water. Both control methods were evaluated based on a simulation of a 110 m inland vessel at different loading conditions when navigating on the river Seine in Paris at different water levels, discharges and wind conditions. The model-based controller gives far more precise control in comparison with human controlled tracks on the same simulator platform than model-free controllers. Therefore, an investigation started on the use of multi-paradigm simulation methods that combine different training and validation platforms for model predictive reinforcement learning in, for example, path following. The principle of this methodology is shown in a simulation on the Western Scheldt and will be further investigated in the DDSHIP, Data-driven smart shipping, project.

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