Integrating LLMs and Digital Twins for Adaptive Multi-Robot Task Allocation in Construction

--Multi-robot systems are emerging as a promising solution to the growing demand for productivity, safety, and adaptability across industrial sectors. However, effectively coordinating multiple robots in dynamic and uncertain environments, such as construction sites, remains a challenge, particularly due to unpredictable factors like material delays, unexpected site conditions, and weather-induced disruptions. T o address these challenges, this study proposes an adaptive task allocation framework that strategically leverages the synergistic potential of Digital Twins, Integer Programming (IP), and Large Language Models (LLMs). The multi-robot task allocation problem is formally defined and solved using an IP model that accounts for task dependencies, robot heterogeneity, scheduling constraints, and re-planning requirements. A mechanism for narrative-driven schedule adaptation is introduced, in which unstructured natural language inputs are interpreted by an LLM, and optimization constraints are autonomously updated, enabling human-in-the-loop flexibility without manual coding. A digital twin-based system has been developed to enable real-time synchronization between physical operations and their digital representations. This closed-loop feedback framework ensures that the system remains dynamic and responsive to ongoing changes on site. A case study demonstrates both the computational efficiency of the optimization algorithm and the reasoning performance of several LLMs, with top-performing models achieving over 97% accuracy in constraint and parameter extraction. The results confirm the practicality, adaptability, and cross-domain applicability of the proposed methods. Ith rising demands for faster project delivery and improved efficiency, automation is becoming an essential solution for the construction industry [1]-[3]. Robotics, particularly the use of coordinated teams of robots, offers a promising approach that could revolutionize traditional construction practices. Robotic systems are being employed on construction sites to assist with tasks such as material delivery [4], assembly [5]-[7], and installation [8], [9], with the potential to significantly improve efficiency [10], [11] and safety [12]. Min Deng is with the Department of Civil, Environmental, and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA (e-mail: mindeng@ttu.edu) Bo Fu is with Amazon Robotics, North Reading, MA 01864, USA (e-mail: bofu@amazon.com) Lingyao Li is with the School of Information, University of South Florida, Tampa, FL 33620, USA (e-mail: lingyaol@usf.edu) Xi Wang is with the Department of Construction Science, Texas A&M University, College Station, TX 77843, USA (e-mail: xiwang@tamu.edu)