Earthwork operations face increasing demand, while workforce aging creates a growing need for automation. ROS2-TMS for Construction, a Cyber-Physical System framework for construction machinery automation, has been proposed; however, its reliance on manually designed Behavior Trees (BTs) limits scalability in cooperative operations. Recent advances in Large Language Models (LLMs) offer new opportunities for automated task planning, yet most existing studies remain limited to simple robotic systems. This paper proposes an LLM-based workflow for automatic generation of BTs toward coordinated operation of construction machines. The method introduces synchronization flags managed through a Global Blackboard, enabling multiple BTs to share execution states and represent inter-machine dependencies. The workflow consists of Action Sequence generation and BTs generation using LLMs. Simulation experiments on 30 construction instruction scenarios achieved up to 93\% success rate in coordinated multi-machine tasks. Real-world experiments using an excavator and a dump truck further demonstrate successful cooperative execution, indicating the potential to reduce manual BTs design effort in construction automation. These results highlight the feasibility of applying LLM-driven task planning to practical earthwork automation.

翻译：土方工程需求日益增长，而劳动力老龄化对自动化提出了更高要求。面向工程机械自动化的信息物理系统框架ROS2-TMS for Construction已被提出，但其依赖人工设计行为树（BTs）的特性限制了协同作业的可扩展性。大型语言模型（LLMs）的最新进展为自动化任务规划提供了新机遇，然而现有研究大多局限于简单机器人系统。本文提出一种基于LLM的工作流程，用于自动生成面向工程机械协同作业的行为树。该方法引入通过全局黑板管理的同步标志，使多个行为树能够共享执行状态并表征机械间的依赖关系。该工作流程包含动作序列生成和基于LLM的行为树生成两个阶段。在30个施工指令场景的仿真实验中，协同多机任务的成功率最高达到93%。使用挖掘机与自卸卡车的实机实验进一步验证了协同作业的成功执行，表明该方法有望减少施工自动化中人工设计行为树的工作量。这些结果凸显了将LLM驱动的任务规划应用于实际土方工程自动化的可行性。