分层观察-定向-决策-行动框架下的不确定环境中的无人机群：框架、潜力与挑战
Hierarchical Observe-Orient-Decide-Act Enabled UAV Swarms in Uncertain Environments: Frameworks, Potentials, and Challenges
AI总结：
本文提出一种分层H-OODA框架，用于提升无人机群在不确定环境中的适应性与效率，通过云-边-终端层的OODA循环和NFV技术实现灵活决策。
摘要：
无人驾驶航空器（UAV）群体正日益被探索用于各种应用，如监视、灾难响应和军事领域。然而，UAV群在动态和不确定环境中实施有效和快速决策面临重大挑战。传统的决策框架主要依赖于集中控制和刚性架构，其适应性和可扩展性在复杂环境中受到限制。为克服这些挑战，本文提出了一种基于分层观察-定向-决策-行动（H-OODA）循环的框架，用于不确定环境中的UAV群操作，该框架通过在云-边-终端层嵌入经典的OODA循环，并利用网络功能虚拟化（NFV）技术提供灵活且可扩展的决策功能。此外，基于所提出的H-OODA框架，我们联合自主决策和协同控制以提高UAV群的适应性和效率。进一步，我们展示了某些典型案例研究以验证所提框架的改进和效率。最后，分析了潜在的挑战和可能的方向，为未来的H-OODA enabled UAV群提供见解。
英文摘要：
Unmanned aerial vehicle (UAV) swarms are increasingly explored for their potentials in various applications such as surveillance, disaster response, and military. However, UAV swarms face significant challenges of implementing effective and rapid decisions under dynamic and uncertain environments. The traditional decision-making frameworks, mainly relying on centralized control and rigid architectures, are limited by their adaptability and scalability especially in complex environments. To overcome these challenges, in this paper, we propose a hierarchical Observe-Orient-Decide-Act (H-OODA) loop based framework for the UAV swarm operation in uncertain environments, which is implemented by embedding the classical OODA loop across the cloud-edge-terminal layers, and leveraging the network function virtualization (NFV) technology to provide flexible and scalable decision-making functions. In addition, based on the proposed H-OODA framework, we joint autonomous decision-making and cooperative control to enhance the adaptability and efficiency of UAV swarms. Furthermore, we present some typical case studies to verify the improvement and efficiency of the proposed framework. Finally, the potential challenges and possible directions are analyzed to provide insights for the future H-OODA enabled UAV swarms.
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。
