用多智能体自动规划柔性产线，省去专家手动配置
AssemPlanner: A Multi-Agent Based Task Planning Framework for Flexible Assembly System
- 基于ReAct框架的智能体动态调整生产流程
- 自然语言输入任务，自动生成可执行操作序列
- 适合智能制造、工业自动化领域研究者
在柔性装配系统中，现有任务规划方法需多名专家耗时配置产线以适应新产品。为此，我们提出一种基于多智能体的任务规划框架AssemPlanner，接受自然语言描述的任务输入，并将其转化为可执行的顺序生产操作。该框架包含SchedAgent、KnowledgeAgent、LineBalanceAgent及场景图等专用智能体。其中SchedAgent作为核心推理引擎，不同于传统静态流程，采用ReAct机制通过多智能体反馈自适应调整动作，结合KnowledgeAgent、LineBalanceAgent与场景图的反馈，自主解决复杂的工业过程约束。为保障可复现性，所有代码与数据集均已公开于https://github.com/chz332/Assemplanner。
原文摘要 · Abstract (English)
In flexible assembly systems, existing task planning methods require a time-consuming configuration process by multiple experts to establish a production line for a new product. To address this challenge, we propose a multi-agent based task planning framework for flexible assembly systems, denoted as AssemPlanner. It takes tasks described in natural language as input, which are then converted into actionable sequential production operations. It comprises several specialized agents, including SchedAgent , KnowledgeAgent, LineBalanceAgent, and a scene graph. Within the proposed framework, SchedAgent serves as the central reasoning engine. Departing from traditional static pipelines, AssemPlanner utilizes a ReAct-based SchedAgent to adaptively adjust actions via multi-agent feedback. By observing the feedback from KnowledgeAgent, LineBalanceAgent, and the scene graph, it autonomously resolves complex industrial process constraints. To facilitate reproducibility, all code and datasets are released at https://github.com/chz332/Assemplanner.
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