A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908394235887616 |
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| author | Nunes, Diana Amorim, Ricardo Ribeiro, Pedro Coelho, André Campos, Rui |
| author_facet | Nunes, Diana Amorim, Ricardo Ribeiro, Pedro Coelho, André Campos, Rui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks Nunes, Diana Amorim, Ricardo Ribeiro, Pedro Coelho, André Campos, Rui Networking and Internet Architecture Robotics 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. |
| title | A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks |
| topic | Networking and Internet Architecture Robotics |
| url | https://arxiv.org/abs/2506.04404 |