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Autonomous Underwater Vehicles (AUVs) are widely utilized in military and civilian applications such as maritime surveillance, reconnaissance, mine detection, and seabed exploration. However, AUV operations face several technical challenges, including real-time optimization, limited underwater communication, and difficulties in maintenance and fault detection. Traditional Modeling and Simulation (M&S) approaches are useful for performance analysis but have limitations in real-time operational optimization. Digital Twin (DT) technology has gained attention as it enables continuous updates of virtual models based on real-time data, allowing for operational optimization. This paper proposes a DT-based approach for AUV operations and analyzes its advantages compared to conventional M&S methods. Specifically, a Lightweight Digital Twin concept is introduced, considering the constraints of underwater communication and computational resources, to enable real-time optimization and predictive maintenance.
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Hyun et al. (2025) studied this question.
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