Agentic AI Meets Edge Computing in Autonomous UAV Swarms

TL;DR

This paper explores integrating large language model-based agentic AI with edge computing to enhance the scalability, resilience, and autonomy of UAV swarms in complex, real-world scenarios like wildfire rescue operations.

Contribution

It proposes three UAV swarm architectures supporting different levels of autonomy and connectivity, and demonstrates the benefits of edge-enabled deployment through a wildfire SAR use case.

Findings

Edge-enabled architecture improves SAR coverage

Reduces mission completion times

Enhances autonomy in UAV swarms

Abstract

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.