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This paper introduces an improved task allocation and formation control system, ARCog-NET, for autonomous UAV swarms, addressing the gap between simulation and real-world deployment. ARCog-NET employs an Edge-Fog-Cloud (EFC) computing model to balance real-time autonomy with high-level optimization, enabling dynamic adjustments to path planning, task allocation, and formation control. Validated using a realistic SITL simulation framework with flight controller firmware and ROS, ARCog-NET demonstrates superior energy efficiency, adaptability, and operational effectiveness compared to conventional swarm methodologies.
UAV swarms can now dynamically optimize data processing based on task urgency and network conditions, thanks to a novel Edge-Fog-Cloud architecture that bridges the gap between theoretical swarm intelligence and real-world applications.
Abstract Most research on UAV swarm architectures remains confined to simulation-based studies, with limited real-world implementation and validation. In order to mitigate this issue, this research presents an improved task allocation and formation control system within ARCog-NET (Aerial Robot Cognitive Architecture), aimed at deploying autonomous UAV swarms as a unified and scalable solution. The proposed architecture integrates perception, planning, decision-making, and adaptive learning, enabling UAV swarms to dynamically adjust path planning, task allocation, and formation control in response to evolving mission demands. Inspired by artificial intelligence and cognitive science, ARCog-NET employs an Edge-Fog-Cloud (EFC) computing model, where edge UAVs handle real-time data acquisition and local processing, fog nodes coordinate intermediate control, and cloud servers manage complex computations, storage, and human supervision. This hierarchical structure balances real-time autonomy at the UAV level with high-level optimization and decision-making, creating a collective intelligence system that automatically fine-tunes decision parameters based on configurable triggers. To validate ARCog-NET, a realistic simulation framework was developed using SITL (Software-In-The-Loop) with actual flight controller firmware and ROS-based middleware, enabling high-fidelity emulation. This framework bridges the gap between virtual simulations and real-world deployments, allowing evaluation of performance in environmental monitoring, search and rescue, and emergency communication network deployment. Results demonstrate superior energy efficiency, adaptability, and operational effectiveness compared to conventional robotic swarm methodologies. By dynamically optimizing data processing based on task urgency, resource availability, and network conditions, ARCog-NET bridges the gap between theoretical swarm intelligence models and real-world UAV applications, paving the way for future large-scale deployments.
