Hierarchical Observe-Orient-Decide-Act Enabled UAV Swarms in Uncertain Environments: Frameworks, Potentials, and Challenges
This addresses the challenge of decision-making for UAV swarms in applications like surveillance and disaster response, but it appears incremental as it builds on existing OODA loops with hierarchical and virtualization enhancements.
The paper tackles the problem of enabling UAV swarms to make effective and rapid decisions in dynamic and uncertain environments by proposing a hierarchical Observe-Orient-Decide-Act (H-OODA) framework, which is implemented across cloud-edge-terminal layers and leverages network function virtualization to enhance adaptability and efficiency, with case studies verifying improvements.
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.