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Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

机译:网络物理应用中的多无人机系统的设计挑战:全面的调查和未来的方向

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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.
机译:无人机(UAV)最近迅速发展,以促进广泛的创新应用,这些创新应用可从根本上改变网络物理系统(CPS)的设计方式。 CPS是新一代的系统,在计算和物理潜力之间具有协同合作,可以通过几种新机制与人类互动。在CPS应用中使用无人机的主要优势在于其非凡的功能,包括其机动性,动态性,轻松部署,自适应高度,敏捷性,可调整性以及随时随地对真实世界功能进行有效评估的能力。此外,从技术角度来看,无人机被认为是高级CPS发展的重要因素。因此,在本次调查中,我们旨在查明用于CPS应用的多无人机系统最基本,最重要的设计挑战。我们重点介绍了关键和通用方面,这些方面涵盖了对目标和基础结构对象的覆盖和跟踪,高能效导航以及使用针对细粒度CPS应用程序的机器学习进行的图像分析。还研究了关键的原型和测试平台,以显示这些实用技术如何促进CPS应用。我们提出并提出了最先进的算法,以定量和定性方法解决设计难题,并通过重要的CPS应用程序绘制这些难题,以就每种应用程序的难题得出有见地的结论。最后,我们总结了可能影响这些领域未来研究的新方向和新思路。

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