开源框架让无人机用自然语言操控，支持本地部署模型
Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent
- 融合PX4飞行控制与ROS 2，通过Ollama运行本地大模型
- 在仿真和自研四轴机上验证，四种LLM生成指令有效
- 适合研究者快速搭建可解释的智能飞行系统
近期自主代理与物理人工智能的发展主要聚焦于人形和轮式机器人，空中机器人仍被忽视。同时，当前主流无人飞行器多模态视觉-语言系统依赖闭源模型，仅限资源丰富的机构使用。为实现自然语言控制无人机的开放化，我们提出一个开源代理框架，集成基于PX4的飞行控制、ROS 2中间件及通过Ollama部署的本地大模型。在仿真环境与自研四轴飞行平台中评估性能，对比了四种大型语言模型（LLM）在指令生成中的表现，以及三种视觉-语言模型（VLM）在场景理解中的能力。
原文摘要 · Abstract (English)
Recent advances in agentic and physical artificial intelligence (AI) have largely focused on ground-based platforms such as humanoid and wheeled robots, leaving aerial robots relatively underexplored. Meanwhile, state-of-the-art unmanned aerial vehicle (UAV) multimodal vision-language systems typically rely on closed-source models accessible only to well-resourced organizations. To democratize natural language control of autonomous drones, we present an open-source agentic framework that integrates PX4-based flight control, Robot Operating System 2 (ROS 2) middleware, and locally hosted models using Ollama. We evaluate performance both in simulation and on a custom quadcopter platform, benchmarking four large language model (LLM) families for command generation and three vision-language model (VLM) families for scene understanding.
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