Hierarchical Observe-Orient-Decide-Act Enabled UAV Swarms in Uncertain Environments: Frameworks, Potentials, and Challenges

TL;DR

This paper introduces a hierarchical OODA loop framework for UAV swarms operating in uncertain environments, enhancing decision-making adaptability and scalability through cloud-edge-terminal integration and network virtualization.

Contribution

It proposes a novel hierarchical OODA framework embedded across multiple layers, utilizing NFV technology for flexible decision-making in UAV swarms.

Findings

Improved decision-making efficiency demonstrated in case studies

Enhanced adaptability of UAV swarms in dynamic environments

Framework scalability validated through simulations

Abstract

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.