IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models

TL;DR

This paper introduces IMR-LLM, a framework leveraging large language models for complex industrial multi-robot task planning and program generation, addressing stricter constraints and dependencies in manufacturing scenarios.

Contribution

The paper presents a novel LLM-driven framework for industrial multi-robot task planning, including a new benchmark and deterministic methods for feasible high-level plans.

Findings

Outperforms existing methods across all evaluation metrics.

Successfully handles complex dependencies and constraints in industrial tasks.

Introduces IMR-Bench, a new benchmark for multi-robot industrial tasks.

Abstract

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.