Agentic AI Meets Edge Computing in Autonomous UAV Swarms
It addresses deployment challenges for UAV swarms in high-risk scenarios like disaster response, though it appears incremental as it builds on existing architectures.
This paper tackles the problem of integrating agentic AI with edge computing to enable scalable and resilient autonomy in UAV swarms, demonstrating in a wildfire search and rescue use case that it achieves high coverage, reduced mission completion times, and increased autonomy compared to traditional methods.
The integration of agentic AI, powered by large language models (LLMs) with autonomous reasoning, planning, and execution, into unmanned aerial vehicle (UAV) swarms opens new operational possibilities and brings the vision of the Internet of Drones closer to reality. However, infrastructure constraints, dynamic environments, and the computational demands of multi-agent coordination limit real-world deployment in high-risk scenarios such as wildfires and disaster response. This paper investigates the integration of LLM-based agentic AI and edge computing to realize scalable and resilient autonomy in UAV swarms. We first discuss three architectures for supporting UAV swarms - standalone, edge-enabled, and edge-cloud hybrid deployment - each optimized for varying autonomy and connectivity levels. Then, a use case for wildfire search and rescue (SAR) is designed to demonstrate the efficiency of the edge-enabled architecture, enabling high SAR coverage, reduced mission completion times, and a higher level of autonomy compared to traditional approaches. Finally, we highlight open challenges in integrating LLMs and edge computing for mission-critical UAV-swarm applications.