Digital Twin System for Multi-Scale Motion Prediction of Unmanned Underwater Vehicles

Unmanned underwater vehicles (UUVs) play a pivotal role in marine applications such as resource exploration, maritime search and rescue. However, communication signal loss remains a critical bottleneck, constraining UUV autonomous operation and mission reliability across four dimensions: navigation, coordination, monitoring, and planning. To address these challenges in communication-denied environments, this paper proposes a UUV digital twin system utilizing motion prediction technology, such as virtual mapping, prediction, and autonomous decision support. Based on a four-layer architecture—comprising the Physical Entity Layer, Virtual Entity Layer, Twin Data & Connectivity Layer, and Services Layer, the system achieves full-state mapping and real-time visualization. Specifically, a hybrid prediction model integrating Transformer and Convolutional Neural Networks (CNN) architectures is developed to extract multi-scale features for resistance prediction, which serves as the critical basis for UUV motion state forecasting. Experimental validation confirms the system’s capability for real-time resistance tracking and high-precision prediction, providing a robust foundation for autonomous navigation control and energy management. These results advance the development of specialized UUV digital twin systems and establish a robust foundation for their engineering applications.