Agentic AI Meets Edge Computing in Autonomous UAV Swarms

2026-01-20

IEEE Internet of Things Magazine

AI Summary

This work addresses the challenge of achieving efficient and autonomous coordination among drone swarms in high-risk, infrastructure-constrained dynamic environments such as wildfire search-and-rescue operations. To this end, it presents the first integration of large language model (LLM)-driven agent AI with edge computing, proposing three scalable and resilient edge-enabled deployment architectures that facilitate low-latency, highly autonomous multi-drone collaboration in mission-critical scenarios. Experimental results demonstrate that the proposed approach significantly improves search coverage, reduces mission completion time, and achieves higher levels of autonomy compared to conventional methods. These findings validate the effectiveness and practicality of synergistically combining LLM-based agents with edge computing for real-time disaster response applications.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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.

Problem

• agentic AI
• edge computing
• UAV swarms
• large language models
• autonomous systems

Innovation

• Agentic AI
• Edge Computing
• UAV Swarms
• Large Language Models
• Autonomous Systems