Earthwork operations are facing an increasing demand, while workforce aging and skill loss create a pressing need for automation. ROS2-TMS for Construction, a Cyber-Physical System framework designed to coordinate construction machinery, has been proposed for autonomous operation; however, its reliance on manually designed Behavior Trees (BTs) limits scalability, particularly in scenarios involving heterogeneous machine cooperation. Recent advances in large language models (LLMs) offer new opportunities for task planning and BT generation. However, most existing approaches remain confined to simulations or simple manipulators, with relatively few applications demonstrated in real-world contexts, such as complex construction sites involving multiple machines. This paper proposes an LLM-based workflow for BT generation, introducing synchronization flags to enable safe and cooperative operation. The workflow consists of two steps: high-level planning, where the LLM generates synchronization flags, and BT generation using structured templates. Safety is ensured by planning with parameters stored in the system database. The proposed method is validated in simulation and further demonstrated through real-world experiments, highlighting its potential to advance automation in civil engineering.

翻译：土方工程作业需求日益增长，而劳动力老龄化与技能流失对自动化提出了迫切需求。为协调工程机械而设计的信息物理系统框架ROS2-TMS for Construction已被提出用于自主作业；然而，其依赖人工设计行为树（BT）的特点限制了可扩展性，在涉及异构机械协同的场景中尤为明显。大型语言模型（LLM）的最新进展为任务规划与行为树生成提供了新的机遇。但现有方法大多局限于仿真环境或简单机械臂，在真实复杂场景（如涉及多台机械的施工现场）中的应用验证相对缺乏。本文提出一种基于LLM的行为树生成工作流，通过引入同步标志实现安全协同作业。该工作流包含两个步骤：高层规划（由LLM生成同步标志）以及基于结构化模板的行为树生成。通过调用系统数据库存储的参数进行规划，确保了操作安全性。所提方法在仿真环境中得到验证，并通过实际场景实验进一步展示其应用潜力，凸显了该方法在推动土木工程自动化进程中的价值。