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Abstract:Uncrewed Aerial Vehicles (UAVs) offer agile, cost-effective, and efficient solutions for communication relay networks. However, their modeling and control are challenging, and the mismatch between simulations and actual conditions limits real-world deployment, while maintaining adequate situational awareness remains essential for safe operation. Several studies have proposed integrating UAV operations with immersive digital technologies, such as Digital Twin (DT) and Extended Reality (XR), to overcome these challenges. This paper provides a comprehensive overview of the latest research and developments involving immersive digital technologies for UAVs. We explore the use of Machine Learning (ML) techniques, particularly Deep Reinforcement Learning (DRL), to improve the capabilities of DT for UAV systems, and present a case study of a DT-driven DRL pipeline that couples bidirectional physical-digital synchronization with online recursive least-squares channel calibration for UAV resource allocation. We further present a second case study in which a diffusion-augmented digital twin, kept statistically faithful to the physical swarm by the same online calibration loop, drives multi-UAV velocity coordination. We identify and discuss key research gaps, and propose countermeasures based on Generative AI (GAI), emphasizing the significant role of AI in advancing DT technology for UAVs. Furthermore, we review and discuss how the XR technology can transform UAV operations with the support of GAI, and examine its practical challenges. Finally, we propose future research directions to further develop the application of immersive digital technologies for UAV operation.

Submission history

From: Yousef Emami [view email]
[v1] Tue, 23 Jul 2024 08:43:28 UTC (33,670 KB)
[v2] Thu, 30 Jan 2025 05:31:09 UTC (35,063 KB)
[v3] Sun, 21 Jun 2026 14:27:59 UTC (806 KB)
[v4] Fri, 17 Jul 2026 05:55:04 UTC (3,363 KB)