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- W4386971258 abstract "Abstract Multiple studies show that humans are the leading cause of maritime accidents. Therefore, the interest in introducing autonomous surface vessels, particularly in the commercial sector, is receiving increasing attention. Digital twins, virtual representations of physics assets or processes enabled through data and models for real-time control, optimization, monitoring, and informed decision-making, are seen as key enablers for autonomy. Based on their capability, a digital twin is generally categorized on a scale from 0–5 (0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous). The current work aims at concretizing the categorization in the context of autonomous vessels. It demonstrates some capability levels by introducing the development of an extendable digital twin framework created in the Unity game engine using physics-based models and real-time data. For this purpose, real-time weather and automatic identification system (AIS) data is streamed. While computer-aided design (CAD) and a three-dimensional (3D) height map build the foundation of a standalone digital twin, real-time data, physics-based models, and synthetic sensors set the basis of descriptive representations. Finally, an environmental disturbance observer enables the first diagnostic abilities, while a reinforcement learning algorithm pushes towards autonomous capabilities. It is argued that the work will improve the overall situational awareness for safe sea operations." @default.
- W4386971258 created "2023-09-23" @default.
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- W4386971258 date "2023-06-11" @default.
- W4386971258 modified "2023-09-29" @default.
- W4386971258 title "Digital Twin for Autonomous Surface Vessels to Generate Situational Awareness" @default.
- W4386971258 doi "https://doi.org/10.1115/omae2023-100626" @default.
- W4386971258 hasPublicationYear "2023" @default.
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