  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.
