Scholarlink

<p>Digital Twin (DT) technology has emerged as a key enabler for optimizing Unmanned Aerial Vehicles (UAV) operations through intelligent decision-making, predictive analytics, and cost-efficient system management. By maintaining real-time synchronization between physical and virtual entities, DTs enable continuous optimization of cyber-physical systems (CPSs) and early detection of potential anomalies. However, existing UAV-DT frameworks often lack comprehensive evaluation methodologies and robust integration mechanisms, limiting their applicability in dynamic flight environments. This paper presents a novel Intelligent Digital Twin Framework for UAVs (IDTF-UAV) designed to enhance autonomy, safety, and decision reliability. The proposed framework integrates an AI-driven model within a four-dimensional architecture, enabling bidirectional data flow and adaptive control between the virtual and physical UAV. IDTF-UAV demonstrates superior performance in real-time flight prediction and control. Simulation results confirm a MAPE of 2.65 %, validating the model’s high accuracy and efficiency. The results highlight its potential as a scalable solution for secure, adaptive, and data-driven UAV management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integration paradigm of intelligent digital twin into UAVs systems

  • 期刊论文

  • 发表日期：

    2026年2月7日

查看全文
  • Fadhila Tlili,
  • Samiha Ayed,
  • Lamia CHAARI FOURATI

摘要

Digital Twin (DT) technology has emerged as a key enabler for optimizing Unmanned Aerial Vehicles (UAV) operations through intelligent decision-making, predictive analytics, and cost-efficient system management. By maintaining real-time synchronization between physical and virtual entities, DTs enable continuous optimization of cyber-physical systems (CPSs) and early detection of potential anomalies. However, existing UAV-DT frameworks often lack comprehensive evaluation methodologies and robust integration mechanisms, limiting their applicability in dynamic flight environments. This paper presents a novel Intelligent Digital Twin Framework for UAVs (IDTF-UAV) designed to enhance autonomy, safety, and decision reliability. The proposed framework integrates an AI-driven model within a four-dimensional architecture, enabling bidirectional data flow and adaptive control between the virtual and physical UAV. IDTF-UAV demonstrates superior performance in real-time flight prediction and control. Simulation results confirm a MAPE of 2.65 %, validating the model’s high accuracy and efficiency. The results highlight its potential as a scalable solution for secure, adaptive, and data-driven UAV management.