UAVGENT: A Language-Guided Distributed Control Framework
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915797733998592 |
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| author | Zhang, Ziyi Deng, Xiyu Qu, Guannan Nakahira, Yorie |
| author_facet | Zhang, Ziyi Deng, Xiyu Qu, Guannan Nakahira, Yorie |
| contents | We study language-in-the-loop control for multi-drone systems that execute evolving, high-level missions while retaining formal robustness guarantees at the physical layer. We propose a three-layer architecture in which (i) a human operator issues natural-language instructions, (ii) an LLM-based supervisor periodically interprets, verifies, and corrects the commanded task in the context of the latest state and target estimates, and (iii) a distributed inner-loop controller tracks the resulting reference using only local relative information. We derive a theoretical guarantee that characterizes tracking performance under bounded disturbances and piecewise-smooth references with discrete jumps induced by LLM updates. Overall, our results illustrate how centralized language-based task reasoning can be combined with distributed feedback control to achieve complex behaviors with provable robustness and stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13212 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UAVGENT: A Language-Guided Distributed Control Framework Zhang, Ziyi Deng, Xiyu Qu, Guannan Nakahira, Yorie Robotics Multiagent Systems Systems and Control We study language-in-the-loop control for multi-drone systems that execute evolving, high-level missions while retaining formal robustness guarantees at the physical layer. We propose a three-layer architecture in which (i) a human operator issues natural-language instructions, (ii) an LLM-based supervisor periodically interprets, verifies, and corrects the commanded task in the context of the latest state and target estimates, and (iii) a distributed inner-loop controller tracks the resulting reference using only local relative information. We derive a theoretical guarantee that characterizes tracking performance under bounded disturbances and piecewise-smooth references with discrete jumps induced by LLM updates. Overall, our results illustrate how centralized language-based task reasoning can be combined with distributed feedback control to achieve complex behaviors with provable robustness and stability. |
| title | UAVGENT: A Language-Guided Distributed Control Framework |
| topic | Robotics Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2602.13212 |