Paper ID: 2411.03465 • Published Nov 5, 2024
Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation
Daniel Menges, Adil Rasheed
TL;DR
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Autonomous surface vessels (ASVs) are becoming increasingly significant in
enhancing the safety and sustainability of maritime operations. To ensure the
reliability of modern control algorithms utilized in these vessels, digital
twins (DTs) provide a robust framework for conducting safe and effective
simulations within a virtual environment. Digital twins are generally
classified on a scale from 0 to 5, with each level representing a progression
in complexity and functionality: Level 0 (Standalone) employs offline modeling
techniques; Level 1 (Descriptive) integrates sensors and online modeling to
enhance situational awareness; Level 2 (Diagnostic) focuses on condition
monitoring and cybersecurity; Level 3 (Predictive) incorporates predictive
analytics; Level 4 (Prescriptive) embeds decision-support systems; and Level 5
(Autonomous) enables advanced functionalities such as collision avoidance and
path following. These digital representations not only provide insights into
the vessel's current state and operational efficiency but also predict future
scenarios and assess life endurance. By continuously updating with real-time
sensor data, the digital twin effectively corrects modeling errors and enhances
decision-making processes. Since DTs are key enablers for complex autonomous
systems, this paper introduces a comprehensive methodology for establishing a
digital twin framework specifically tailored for ASVs. Through a detailed
literature survey, we explore existing state-of-the-art enablers across the
defined levels, offering valuable recommendations for future research and
development in this rapidly evolving field.