An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations
Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10876184/ |
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| author | Nikos Kougiatsos Evelien L. Scheffers Marcel C. van Benten Dingena L. Schott Peter de Vos Rudy R. Negenborn Vasso Reppa |
| author_facet | Nikos Kougiatsos Evelien L. Scheffers Marcel C. van Benten Dingena L. Schott Peter de Vos Rudy R. Negenborn Vasso Reppa |
| author_sort | Nikos Kougiatsos |
| collection | DOAJ |
| description | Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine vessels before and during a mission. The connection between the system architecture and control design perspectives is enabled using a semantics-based technique. To this end, the multi-level vessel control system is described by a semantic database, a knowledge graph used to connect the components automatically, and quantitative service criteria. Considering the system architecture, the optimal modification is deduced using modularity and complexity criteria, originating from the field of network theory. On the control side, an intelligent automation supervisor is designed to make offline and online decisions regarding the energy deficit to execute a new mission and the active automation configuration during operation. For offline decisions, system architecture modifications are requested by the vessel designers to cover the energy deficit. During operation, switching between hardware and virtual sensors as well as switching between energy management controllers is implemented to handle the effects of sensor faults. The framework is successfully applied to a case study of a tugboat used to adapt to missions with different power requirements, while simulation results are used to indicate its application in supporting the decisions of vessel designers and human vessel operators. |
| format | Article |
| id | doaj-art-e02e6b7dbfbd41c9a653ed2b0f0e345e |
| institution | DOAJ |
| issn | 2687-7813 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-e02e6b7dbfbd41c9a653ed2b0f0e345e2025-08-20T03:02:15ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132025-01-01618420310.1109/OJITS.2025.353941910876184An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission AdaptationsNikos Kougiatsos0https://orcid.org/0000-0001-9518-9114Evelien L. Scheffers1Marcel C. van Benten2https://orcid.org/0009-0000-0668-6988Dingena L. Schott3Peter de Vos4Rudy R. Negenborn5https://orcid.org/0000-0001-9784-1225Vasso Reppa6https://orcid.org/0000-0002-8599-6016Department of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsDepartment of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsDepartment of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsDepartment of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsDepartment of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsDepartment of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsDepartment of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The NetherlandsWaterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine vessels before and during a mission. The connection between the system architecture and control design perspectives is enabled using a semantics-based technique. To this end, the multi-level vessel control system is described by a semantic database, a knowledge graph used to connect the components automatically, and quantitative service criteria. Considering the system architecture, the optimal modification is deduced using modularity and complexity criteria, originating from the field of network theory. On the control side, an intelligent automation supervisor is designed to make offline and online decisions regarding the energy deficit to execute a new mission and the active automation configuration during operation. For offline decisions, system architecture modifications are requested by the vessel designers to cover the energy deficit. During operation, switching between hardware and virtual sensors as well as switching between energy management controllers is implemented to handle the effects of sensor faults. The framework is successfully applied to a case study of a tugboat used to adapt to missions with different power requirements, while simulation results are used to indicate its application in supporting the decisions of vessel designers and human vessel operators.https://ieeexplore.ieee.org/document/10876184/Decision support systemsintelligent systemsknowledge-representation techniquesresilient operationnetwork theory (graphs)marine safety |
| spellingShingle | Nikos Kougiatsos Evelien L. Scheffers Marcel C. van Benten Dingena L. Schott Peter de Vos Rudy R. Negenborn Vasso Reppa An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations IEEE Open Journal of Intelligent Transportation Systems Decision support systems intelligent systems knowledge-representation techniques resilient operation network theory (graphs) marine safety |
| title | An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations |
| title_full | An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations |
| title_fullStr | An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations |
| title_full_unstemmed | An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations |
| title_short | An Intelligent Agent-Based Resilient Framework for Marine Vessel Mission Adaptations |
| title_sort | intelligent agent based resilient framework for marine vessel mission adaptations |
| topic | Decision support systems intelligent systems knowledge-representation techniques resilient operation network theory (graphs) marine safety |
| url | https://ieeexplore.ieee.org/document/10876184/ |
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