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Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent
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Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent
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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.
Forward citations
Cited by 3 Pith papers
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RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control
An architecture that mediates LLM computer-use agents for UAV control by compiling agent decisions into validated, time-bounded, evidence-logged skill invocations, with a prototype on OpenClaw/PX4/OP-TEE.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
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A Universal Large Language Model -- Drone Command and Control Interface
A universal LLM-to-drone interface is implemented via the Model Context Protocol (MCP) and Mavlink, demonstrated with real UAV flight control and simulated flights using live map data.
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