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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Nunes, Diana (Autor:in) / Amorim, Ricardo (Autor:in) / Ribeiro, Pedro (Autor:in) / Coelho, Andre (Autor:in) / Campos, Rui (Autor:in)


    Erscheinungsdatum :

    07.07.2025


    Format / Umfang :

    3989381 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception

    Karagounis, Athanasios | ArXiv | 2024

    Freier Zugriff

    Leveraging Large Language Models for Tradespace Exploration

    Apaza, Gabriel / Selva, Daniel | AIAA | 2024



    A Superalignment Framework in Autonomous Driving with Large Language Models

    Kong, Xiangrui / Braunl, Thomas / Fahmi, Marco et al. | IEEE | 2024