用AI把静态BIM变动态数字孪生，让机器人施工更安全高效。
Building Information Models to Robot-Ready Site Digital Twins (BIM2RDT): An Agentic AI Safety-First Framework
- 用大模型推理物体朝向先验，提升点云对齐精度。
- 点云对齐误差降低64.3%~88.3%，即使在遮挡情况下仍准确。
- 集成振动监测与安全标准，实时预警，适合智能工地应用。
本文提出BIM2RDT框架，将静态建筑信息模型（BIM）转化为可驱动机器人的动态数字孪生（DT），以安全为首要原则。该框架融合三类数据：来自BIM的几何与语义信息、物联网传感器的活动数据、以及机器人巡检获取的视觉空间数据。提出基于大语言模型（LLM）推理的语义引力ICP（SG-ICP）算法，利用BIM语义推断物体合理朝向先验，避免传统ICP陷入局部最优，显著提升对齐精度。实验显示，在存在遮挡的场景下，SG-ICP使均方根误差（RMSE）降低64.3%至88.3%。同时集成实时手-臂振动（HAV）监测，依据ISO 5349-1标准将传感器安全事件映射至数字孪生，触发警报。机器人采集数据反馈更新数字孪生，后者反向优化任务路径，形成闭环。
原文摘要 · Abstract (English)
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 construction industry. This paper introduces BIM2RDT (Building Information Models to Robot-Ready Site Digital Twins), 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 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, activity data from IoT sensor networks, and visual-spatial data collected by robots during site traversal. The methodology introduces Semantic-Gravity ICP (SG-ICP), a point cloud registration algorithm that leverages large language model (LLM) reasoning. Unlike traditional methods, SG-ICP utilizes an LLM to infer object-specific, plausible orientation priors based on BIM semantics, improving alignment accuracy by avoiding convergence on local minima. This creates a feedback loop where robot-collected data updates the DT, which in turn optimizes paths for missions. The framework employs YOLOE object detection and Shi-Tomasi corner detection to identify and track construction elements while using BIM geometry as a priori maps. The framework also integrates real-time Hand-Arm Vibration (HAV) monitoring, mapping sensor-detected safety events to the digital twin using IFC standards for intervention. Experiments demonstrate SG-ICP's superiority over standard ICP, achieving RMSE reductions of 64.3%--88.3% in alignment across scenarios with occluded features, ensuring plausible orientations. HAV integration triggers warnings upon exceeding exposure limits, enhancing compliance with ISO 5349-1.
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