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This paper introduces a hierarchical Observe-Orient-Decide-Act (H-OODA) loop framework for UAV swarm operation in uncertain environments, distributing the classical OODA loop across cloud-edge-terminal layers. The framework leverages network function virtualization (NFV) to enable flexible and scalable decision-making. Case studies demonstrate the improved adaptability and efficiency of UAV swarms using the proposed H-OODA framework with joint autonomous decision-making and cooperative control.
A hierarchical OODA loop architecture can significantly improve the adaptability and efficiency of UAV swarms operating in dynamic, uncertain environments.
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.
