基于技能的智能制造中的LLM驱动多智能体控制
LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing
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中文总结 AI 辅助
针对小批量定制化生产中频繁重编程的需求，提出将LLM智能体与MCP工具结合，通过MQTT协调和实时状态注入实现智能制造，实验表明多种架构均达到93%解决率并涌现故障诊断能力。
中文摘要 AI 辅助
工厂正转向小批量、高产品定制化的生产模式，这要求频繁重新编程灵活且可重构的自动化系统。基于LLM的智能体可部署在两种互补角色中：离线时，它们生成确定性的生产序列，减少编程工作量；在线时，它们操作实时机器并处理静态程序无法预见的运行时故障。我们提出一种解决方案，其中每个工厂模块配备一个专用的基于LLM的智能体和一个MCP工具服务器，该服务器通过OPC UA方法调用暴露模块的技能，智能体通过MQTT进行协调，并基于工厂状态的实时更新进行接地。我们在一个物理六模块六边形工厂的仿真中，比较了三种智能体架构（编排者、点对点和单体），涵盖九个复杂度递增的生产挑战，包括静默硬件故障检测。单体架构和点对点架构均达到最高的平均解决率（93%），而编排者架构在全部十次运行中独特地解决了静默传送带故障，通过自主地将板材绕开阻塞段重新路由。所有架构均表现出涌现的故障诊断行为，无需任何显式的故障处理逻辑，从而确立了标准化的MCP工具、基于MQTT的智能体间通信以及实时状态注入作为LLM编程智能制造可行且可复现的基础。
英文摘要
Factories are shifting toward smaller lot sizes with high product customization, requiring frequent re-programming of flexible and reconfigurable automation systems. LLM-based agents can be deployed in two complementary roles: Offline, they generate deterministic production sequences, reducing programming effort; online, they operate live machines and handle unforeseen runtime faults that static programs cannot anticipate. We propose a solution in which each factory module is paired with a dedicated LLM-based agent and an MCP tool server that exposes the module's skills via OPC UA method calls, with agents coordinating over MQTT and grounded by real-time updates of the factory state. We compare three agent architectures (orchestrator, peer-to-peer, and monolithic) across nine production challenges of increasing complexity in a simulation of a physical six-module hexagonal factory, including silent hardware fault detection. The monolithic and peer-to-peer architectures both achieve the highest mean solve rate (93\%), while the orchestrator uniquely resolves a silent conveyor-belt fault in all ten runs by autonomously rerouting plates around the blocked segment. All architectures exhibit emergent fault-diagnosis behavior without any explicit failure-handling logic, establishing standardized MCP tooling, MQTT-based inter-agent communication, and real-time state injection as a viable and reproducible foundation for LLM-programmed smart manufacturing.
发表机构
- Technical University of Munich（慕尼黑工业大学）
- Siemens AG（西门子股份公司）
机构由 AI 辅助整理，请以论文原文为准。
