ARCog-NET: An Aerial Robot Cognitive Network Architecture for Swarm Applications Development

This work presents an advanced cognitive architecture for networked aerial robots to implement autonomous swarm systems effectively. It focuses on designing, implementing, and evaluating an architecture that enables unmanned aerial vehicles (UAVs) to coordinate and cooperate for complex tasks, with or without human intervention. Inspired by artificial intelligence, cognitive science, and robotics, the architecture integrates perception, planning, decision-making, and adaptive learning to optimize swarm behavior in dynamic environments. The architecture uses a distributed processing model based on the “edge-fog-cloud” (EFC) concept. Edge-level robots handle real-time data collection, local decision-making, and environmental perception. Fog-level vehicles manage intermediate processing and supervision of the groups, while cloud servers perform comprehensive data analysis and long-term storage, being the higher-level hierarchy of the framework. This structure allows efficient distribution of computational tasks, with critical decisions made at the robot level and complex analysis done in the fog or cloud. The implementation of ARCog-NET involves deploying a multi-agent simulation system using the Robot Operating System (ROS) and Gazebo simulator, facilitating the integration of sensors, communication protocols, and data processing algorithms. The performance evaluation demonstrates the architecture’s effectiveness in a wind farm inspection scenario, where the UAV swarm exhibits improved trajectory planning, collision avoidance, and data processing efficiency. Simulation results show that ARCog-NET reduces latency, increases data throughput, and enhances operational effectiveness, providing a robust platform for future developers to focus on applications and direct robot control methods.