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.

翻译：无人机集群在监视、灾害响应和军事等领域的应用潜力日益受到关注。然而，无人机集群在动态不确定环境下实现有效快速决策面临重大挑战。传统决策框架主要依赖集中控制和刚性架构，其适应性和可扩展性尤其在复杂环境中受到限制。为克服这些挑战，本文提出一种基于分层观察-定向-决策-执行循环的框架，用于不确定环境下的无人机集群操作。该框架通过将经典OODA循环嵌入云-边-端层级，并利用网络功能虚拟化技术提供灵活可扩展的决策功能。此外，基于所提出的H-OODA框架，我们结合自主决策与协同控制以增强无人机集群的适应性和效率。进一步，我们通过典型案例研究验证了所提框架的改进效果与运行效率。最后，本文分析了潜在挑战与可能发展方向，为未来基于H-OODA的无人机集群研究提供参考。