Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909643164352512 |
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| author | Lim, Shoon Kit Chong, Melissa Jia Ying Khor, Jing Huey Ling, Ting Yang |
| author_facet | Lim, Shoon Kit Chong, Melissa Jia Ying Khor, Jing Huey Ling, Ting Yang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_07509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent Lim, Shoon Kit Chong, Melissa Jia Ying Khor, Jing Huey Ling, Ting Yang Robotics I.2.7; I.2.9; I.2.10 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. |
| title | Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent |
| topic | Robotics I.2.7; I.2.9; I.2.10 |
| url | https://arxiv.org/abs/2506.07509 |