An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework

2025-04-20

AI Summary

Existing LLM-based multi-agent frameworks are confined to digital or simulation environments, lacking support for physically realizable engineering design tasks that require cross-disciplinary collaboration and rigorous constraint-aware reasoning. Method: This paper proposes a physics-aware multi-agent collaborative framework for physical system development, integrating role-based agent architecture, constraint-aware planning, modular domain-specific agents (mechanical, electronic, control, and software), and human-in-the-loop feedback mechanisms. Contribution/Results: The framework enables, for the first time, end-to-end autonomous design of electromechanical systems driven by LLMs—bridging the gap between simulation and physical implementation while ensuring feasibility and joint cross-domain constraint reasoning. We empirically validate it through the fully autonomous design and prototyping of an automated water-quality monitoring and sampling vessel, featuring optimized propulsion, cost-effective hardware, and advanced control—all orchestrated by collaborative agents with minimal expert intervention.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multi-Robot Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Extends LLM multi-agent frameworks to physical mechatronics design
• Integrates cross-disciplinary expertise for autonomous prototype generation
• Reduces human input in complex engineering tasks via language-driven workflow

Innovation

• Multi-agent framework integrates cross-disciplinary expertise
• Language-driven workflow with structured human feedback
• Specialized agents automate real-world engineering design