An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912337819074560 |
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| author | Wang, Zeyu Lo, Frank P. -W. Chen, Qian Zhang, Yongqi Lin, Chen Chen, Xu Yu, Zhenhua Thompson, Alexander J. Yeatman, Eric M. Lo, Benny P. L. |
| author_facet | Wang, Zeyu Lo, Frank P. -W. Chen, Qian Zhang, Yongqi Lin, Chen Chen, Xu Yu, Zhenhua Thompson, Alexander J. Yeatman, Eric M. Lo, Benny P. L. |
| contents | Existing LLM-enabled multi-agent frameworks are predominantly limited to digital or simulated environments and confined to narrowly focused knowledge domain, constraining their applicability to complex engineering tasks that require the design of physical embodiment, cross-disciplinary integration, and constraint-aware reasoning. This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering to autonomously generate functional prototypes with minimal direct human design input. Operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints. To validate its capabilities, the framework is applied to a real-world challenge involving autonomous water-quality monitoring and sampling, where traditional methods are labor-intensive and ecologically disruptive. Leveraging the proposed system, a fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control. The design process was carried out by specialized agents, including a high-level planning agent responsible for problem abstraction and dedicated agents for structural, electronics, control, and software development. This approach demonstrates the potential of LLM-based multi-agent systems to automate real-world engineering workflows and reduce reliance on extensive domain expertise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework Wang, Zeyu Lo, Frank P. -W. Chen, Qian Zhang, Yongqi Lin, Chen Chen, Xu Yu, Zhenhua Thompson, Alexander J. Yeatman, Eric M. Lo, Benny P. L. Robotics Artificial Intelligence Existing LLM-enabled multi-agent frameworks are predominantly limited to digital or simulated environments and confined to narrowly focused knowledge domain, constraining their applicability to complex engineering tasks that require the design of physical embodiment, cross-disciplinary integration, and constraint-aware reasoning. This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering to autonomously generate functional prototypes with minimal direct human design input. Operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints. To validate its capabilities, the framework is applied to a real-world challenge involving autonomous water-quality monitoring and sampling, where traditional methods are labor-intensive and ecologically disruptive. Leveraging the proposed system, a fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control. The design process was carried out by specialized agents, including a high-level planning agent responsible for problem abstraction and dedicated agents for structural, electronics, control, and software development. This approach demonstrates the potential of LLM-based multi-agent systems to automate real-world engineering workflows and reduce reliance on extensive domain expertise. |
| title | An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.14681 |