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Advancing UAV swarm autonomy with ARCog-NET for task allocation, path planning, and formation control

Published online by Cambridge University Press:  07 July 2025

Gabryel Silva Ramos Open the ORCID record for Gabryel Silva Ramos [Opens in a new window] ,
Milena Faria Pinto Open the ORCID record for Milena Faria Pinto [Opens in a new window]  and
Diego Barreto Haddad Open the ORCID record for Diego Barreto Haddad [Opens in a new window]
Gabryel Silva Ramos*
Affiliation:
Federal Center for Technological Education Celso Suckow da Fonseca (CEFET-RJ), Rio de Janeiro, Brazil
Milena Faria Pinto
Affiliation:
Federal Center for Technological Education Celso Suckow da Fonseca (CEFET-RJ), Rio de Janeiro, Brazil
Diego Barreto Haddad
Affiliation:
Federal Center for Technological Education Celso Suckow da Fonseca (CEFET-RJ), Rio de Janeiro, Brazil
*
Corresponding author: Gabryel Silva Ramos; Email: gabryelsr@gmail.com
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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.

Keywords

cognitive architectureaerial robotsedge-fog-cloud technologiesswarm systemsdistributed data processingautonomy of aerial robots

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 45
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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