用大模型实现多机器人产线故障时的实时任务重分配
Dynamic Task Adaptation for Multi-Robot Manufacturing Systems with Large Language Models
- 大模型解析机器人配置，动态生成任务重分配方案
- 实测故障恢复任务成功率高，具备强适应能力
- 适合需要灵活应对突发状况的智能工厂场景
当前制造系统越来越多采用多机器人协作应对复杂动态环境。尽管多智能体架构支持机器人间的去中心化协同，但在无预设规则情况下难以实现对意外中断的实时适应。大语言模型的进展为上下文感知决策提供了新机遇，可实现对突发变化的自适应响应。本文提出一种基于大语言模型的控制框架，用于多机器人制造系统中的动态任务重分配。中心控制器利用大模型理解结构化机器人配置数据，并在机器人故障时生成有效重分配方案。真实场景实验表明，该方法在故障恢复中表现出高任务成功率，展示了其提升多机器人制造系统适应性的潜力。
原文摘要 · Abstract (English)
Recent manufacturing systems are increasingly adopting multi-robot collaboration to handle complex and dynamic environments. While multi-agent architectures support decentralized coordination among robot agents, they often face challenges in enabling real-time adaptability for unexpected disruptions without predefined rules. Recent advances in large language models offer new opportunities for context-aware decision-making to enable adaptive responses to unexpected changes. This paper presents an initial exploratory implementation of a large language model-enabled control framework for dynamic task reassignment in multi-robot manufacturing systems. A central controller agent leverages the large language model's ability to interpret structured robot configuration data and generate valid reassignments in response to robot failures. Experiments in a real-world setup demonstrate high task success rates in recovering from failures, highlighting the potential of this approach to improve adaptability in multi-robot manufacturing systems.
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