A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks

TL;DR

This paper introduces FLUC, a modular framework that uses large language models to translate natural language commands into UAV mission code, enabling autonomous control in flying networks with demonstrated effectiveness.

Contribution

The paper presents FLUC, a novel framework integrating open-source LLMs with UAV autopilot systems for natural language-driven autonomous UAV control.

Findings

Qwen 2.5 excels in multi-step reasoning

Gemma 2 balances accuracy and latency

LLaMA 3.2 offers faster responses with lower coherence

Abstract

This paper proposes FLUC, a modular framework that integrates open-source Large Language Models (LLMs) with Unmanned Aerial Vehicle (UAV) autopilot systems to enable autonomous control in Flying Networks (FNs). FLUC translates high-level natural language commands into executable UAV mission code, bridging the gap between operator intent and UAV behaviour. FLUC is evaluated using three open-source LLMs - Qwen 2.5, Gemma 2, and LLaMA 3.2 - across scenarios involving code generation and mission planning. Results show that Qwen 2.5 excels in multi-step reasoning, Gemma 2 balances accuracy and latency, and LLaMA 3.2 offers faster responses with lower logical coherence. A case study on energy-aware UAV positioning confirms FLUC's ability to interpret structured prompts and autonomously execute domain-specific logic, showing its effectiveness in real-time, mission-driven control.