This paper proposes FLUC, a modular frame-work 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.


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    Title :

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


    Contributors:


    Publication date :

    2025-07-07


    Size :

    3989381 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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