用大模型让无人机自主听懂指令，实时执行任务
A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks
- 将自然语言指令转为可执行的飞行代码，连接操作意图与飞行行为
- Qwen 2.5 最擅长多步推理，Gemma 2 平衡准确与速度，LLaMA 3.2 响应最快但逻辑稍弱
- 适合想快速部署智能无人机系统的研发团队使用
本文提出FLUC框架，通过集成开源大语言模型（LLMs）与无人机自动控制系统，实现飞行网络中的自主控制。该框架将高层自然语言命令转化为可执行的无人机任务代码，弥合操作者意图与无人机行为之间的差距。在代码生成和任务规划场景中，对Qwen 2.5、Gemma 2和LLaMA 3.2三款开源模型进行了评估。结果表明，Qwen 2.5在多步推理方面表现最优，Gemma 2在准确率与延迟间取得良好平衡，而LLaMA 3.2响应更快但逻辑一致性较低。一项关于能耗感知无人机定位的案例研究验证了FLUC对结构化提示的理解能力及自主执行领域特定逻辑的能力，证明其在实时、任务驱动控制中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。