AssemPlanner: A Multi-Agent Based Task Planning Framework for Flexible Assembly System
This work addresses the bottleneck of slow reconfiguration in flexible assembly systems for manufacturers needing to quickly adapt to new products.
AssemPlanner is a multi-agent framework that converts natural language task descriptions into sequential production operations for flexible assembly systems, eliminating the need for time-consuming expert configuration. It uses a ReAct-based central agent to adaptively adjust actions via multi-agent feedback, autonomously resolving complex industrial constraints.
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.