Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks. The proposed system adapts ChatGPT for construction sequence planning and demonstrate its feasibility and effectiveness through experimental evaluation including Two case studies and 80 trials about real construction tasks. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly. This paper contributes to the ongoing efforts to enhance the capabilities and performance of robot-based assembly systems in the construction industry, and it paves the way for further integration of large language model technologies in the field of construction robotics.

翻译：机器人装配在建筑领域已成为应对成本上升、劳动力短缺以及安全高效施工需求等挑战的一种有前景的解决方案。实现这些机器人系统全部潜力的主要障碍之一，是需要在施工任务中进行有效且高效的序列规划。当前的方法，包括数学与启发式技术或机器学习方法，在动态施工环境中的适应性和可扩展性方面存在局限性。为拓展当前机器人系统在序列理解方面的能力，本文引入了RoboGPT——一种利用大型语言模型ChatGPT的高级推理能力，针对机器人装配施工任务实现自动序列规划的新系统。该系统使ChatGPT适用于施工序列规划，并通过包括两个案例研究及80次真实施工任务试验在内的实验评估，验证了其可行性与有效性。结果表明，由RoboGPT驱动的机器人能够处理复杂的施工作业，并实时适应变化。本文致力于提升建筑行业中机器人装配系统的能力与性能，同时为大型语言模型技术在建筑机器人领域的进一步整合奠定了基础。