Building Information Models to Robot-Ready Site Digital Twins (BIM2RDT): An Agentic AI Safety-First Framework

ABSTRACT The adoption of cyber-physical systems and jobsite intelligence that connects design models, real-time site sensing, and autonomous field operations can dramatically enhance digital management in the Architecture, Engineering, and Construction (AEC) industry. This paper introduces BIM2RDT (Building Information Models to Robot-Ready Site Digital T wins), an agentic artificial intelligence (AI) framework designed to transform static Building Information Modeling (BIM) into dynamic, robot-ready digital twins (DTs) that prioritize safety during construction execution. The framework bridges the gap between pre-existing BIM data and real-time site conditions by integrating three key data streams: geometric and semantic information from BIM models, real-time activity data from IoT sensor networks, and visual-spatial data collected by quadruped robots during site traversal. The methodology introduces Semantic-Gravity ICP (SG-ICP), a novel point cloud registration algorithm that leverages large language model (LLM) reasoning. This creates an intelligent feedback loop where robot-collected data updates the DT, which in turn optimizes paths for subsequent missions. The framework employs YOLOE open-vocabulary object detection and Shi-Tomasi corner detection to identify and track construction elements while using BIM geometry as robust a priori maps. Major findings from experiments demonstrate SG-ICP's superiority over standard ICP, achieving RMSE reductions of 64.3%-88.3% in alignment across varied scenarios with occluded or sparse features, ensuring physically plausible orientations. HA V integration triggers real-time warnings and tasks upon exceeding exposure limits, enhancing compliance with such standards as ISO 5349-1. PRACTICAL APPLICATIONS Construction sites are becoming increasingly complex with the introduction of new technologies such as reality capture equipment and robots, requiring better tools to streamline adoption, avoid tool sprawl, and ensure worker safety. This research introduces a system that combines robots, smart sensors, and building information modeling (BIM) data to create a "digital twin": an up-to-date virtual copy of a construction site's geometries and safety information. The system uses quadruped robots equipped with cameras and sensors to autonomously walk through construction sites, automatically detecting and tracking objects like equipment, materials, and temporary structures. Unlike traditional approaches that start from scratch, this method leverages existing BIM data as a foundation, making the robots more accurate and efficient at understanding their surroundings. Besides geometric site updates, safety information is also presented in the updated digital twin.