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.
@article{arxiv.2506.07509,
title = {Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent},
author = {Shoon Kit Lim and Melissa Jia Ying Chong and Jing Huey Khor and Ting Yang Ling},
journal= {arXiv preprint arXiv:2506.07509},
year = {2025}
}
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Source code available at: https://github.com/limshoonkit/ros2-agent-ws