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.
@article{arxiv.2605.08831,
title = {AssemPlanner: A Multi-Agent Based Task Planning Framework for Flexible Assembly System},
author = {Chenhao Zhang and Chaoran Zhang and Zhaobo Xu and Yongbo Yang and Pingfa Feng and Long Zeng},
journal= {arXiv preprint arXiv:2605.08831},
year = {2026}
}