自主水面船舶的数字孪生：安全海上导航的使能器
Digital Twin for Autonomous Surface Vessels: Enabler for Safe Maritime Navigation
AI总结：
本文提出针对自主水面船舶的数字孪生框架方法论，涵盖0至5级分类，通过实时数据更新提升安全导航与决策能力。
摘要：
自主水面船舶（ASVs）在提升海上作业的安全性和可持续性方面正变得越来越重要。为确保这些船舶中使用的现代控制算法的可靠性，数字孪生（DTs）提供了一个在虚拟环境中进行安全有效仿真的稳健框架。数字孪生通常按0至5级分类，每个级别代表复杂性和功能性的递进：0级（独立）采用离线建模技术；1级（描述性）集成传感器和在线建模以增强态势感知；2级（诊断性）侧重于状态监测和网络安全；3级（预测性）纳入预测分析；4级（处方性）嵌入决策支持系统；5级（自主性）实现避碰和路径跟踪等高级功能。这些数字表示不仅提供对船舶当前状态和运行效率的洞察，还能预测未来情景并评估寿命耐久性。通过实时传感器数据的持续更新，数字孪生有效纠正建模误差并增强决策过程。由于DTs是复杂自主系统的关键使能器，本文介绍了一种专门针对ASVs建立数字孪生框架的综合方法论。通过详细的文献调查，我们探索了所定义级别上的现有最先进使能器，为这一快速发展领域的未来研究和发展提供了有价值的建议。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
