Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
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Abstract (arXiv)
Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
7 papers.
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 28
- 2021Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Controlcited by 3
- 2021Machine Learning Methods for Management UAV Flocks -- a Surveycited by 2
- 2021A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environmentscited by 1
- 2024A Task-Driven Multi-UAV Coalition Formation Mechanismcited by 0
- 2024Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A Distributed Strategy Generalized to Arbitrary User Distributionscited by 0
- 2026Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflictcited by 0
- 2026Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict15 Jun 2026
- 2024Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A Distributed Strategy Generalized to Arbitrary User Distributions7 Nov 2024
- 2024A Task-Driven Multi-UAV Coalition Formation Mechanism8 Mar 2024
- 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey7 Nov 2022
- 2021Machine Learning Methods for Management UAV Flocks -- a Survey30 Aug 2021
- 2021A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments19 Jul 2021
- 2021Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control8 Mar 2021