用大模型解决工业多机器人任务规划与程序生成难题
IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models
- 通过构建析取图并结合确定性求解，生成高效高层任务计划
- 在三个复杂度层级的挑战性基准上性能全面超越现有方法
- 适合工业自动化、机器人协同系统研发人员参考
在现代工业生产中，多个机器人常需协作完成复杂制造任务。大语言模型（LLMs）凭借强大的推理能力，在家庭和操作任务中已展现协调机器人潜力。然而，工业场景中更严格的顺序约束和任务间复杂依赖给LLMs带来新挑战。为此，我们提出IMR-LLM，一种基于大语言模型的工业多机器人任务规划与程序生成框架。具体地，利用LLMs辅助构建析取图，并采用确定性求解方法获得可行且高效的高层任务计划；在此基础上，使用过程树引导LLMs生成可执行的低层程序。此外，我们构建了IMR-Bench，一个涵盖三类复杂度的多机器人工业任务挑战性基准。实验结果表明，该方法在所有评估指标上均显著优于现有方法。
原文摘要 · Abstract (English)
In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics.
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