[论文解读] Enabling Building Information Model-Driven Human-Robot Collaborative Construction Workflows with Closed-Loop Digital Twins

Xi Wang, Hongrui Yu+3 more|arXiv (Cornell University)|Jun 16, 2023

Innovations in Concrete and Construction Materials参考文献 73被引用 7

一句话总结

本文提出了一种闭环数字孪生框架，将建筑信息模型（BIM）与建筑施工中的人机协作相结合，使机器人能够基于实时现场数据自适应地调整计划，同时人类工人在需要时进行监督和干预。通过持续将实际建造信息更新至BIM，该系统在实际施工中提升了鲁棒性和准确性，相关有效性通过在实验室和仿真环境中使用机械臂进行的石膏板安装案例研究得到验证。

ABSTRACT

The introduction of assistive construction robots can significantly alleviate physical demands on construction workers while enhancing both the productivity and safety of construction projects. Leveraging a Building Information Model (BIM) offers a natural and promising approach to driving robotic construction workflows. However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built components, robots cannot solely rely on a BIM to plan and perform field construction work. Human workers are adept at improvising alternative plans with their creativity and experience and thus can assist robots in overcoming uncertainties and performing construction work successfully. In such scenarios, it is critical to continuously update the BIM as work processes unfold so that it includes as-built information for the ensuing construction and maintenance tasks. This research introduces an interactive closed-loop digital twin framework that integrates a BIM into human-robot collaborative construction workflows. The robot's functions are primarily driven by the BIM, but it adaptively adjusts its plans based on actual site conditions, while the human co-worker oversees and supervises the process. When necessary, the human co-worker intervenes to help the robot overcome the encountered uncertainties. A drywall installation case study is conducted to verify the proposed workflow. In addition, experiments are carried out to evaluate the system performance using an industrial robotic arm in a research laboratory setting that mimics a construction site and in the Gazebo simulation. Integrating the flexibility of human workers and the autonomy and accuracy afforded by the BIM, the proposed framework offers significant promise of increasing the robustness of construction robots in the performance of field construction work.

研究动机与目标

• 解决由于设计BIM模型与实际建造现场条件之间存在差异，导致的机器人施工工作流程中的差距问题。
• 通过将实时现场反馈整合到基于BIM的规划中，提升施工机器人的鲁棒性。
• 在机器人执行过程中出现不确定性时，实现人类工人监督与干预的人机协作。
• 通过持续将实际建造数据更新至BIM，实现物理施工与数字模型之间的闭环。
• 通过在实验室和仿真环境中进行的石膏板安装案例研究，验证该框架的有效性。

提出的方法

• 该框架以BIM作为机器人施工任务的中心规划权威。
• 数字孪生通过传感器持续监控现场状况，并将其与BIM进行对比，以检测偏差。
• 当检测到偏差时，机器人基于实时数据动态重新规划其动作，同时接受人类监督。
• 当机器人无法解决不确定性时，人类工人进行干预，以确保任务连续性和安全性。
• 每次操作后，系统将实际建造数据更新至BIM，以保持数字模型与物理模型的一致性。
• 该框架通过工业级机械臂在实验室环境中以及Gazebo仿真环境中进行评估，以模拟真实施工条件。

实验结果

研究问题

• RQ1 如何使基于BIM的机器人施工工作流程对现实世界中的现场不确定性具备鲁棒性？
• RQ2 当出现偏差时，人类工人可在机器人操作中发挥何种监督与干预作用？
• RQ3 数字孪生如何确保实际建造现场与设计BIM之间的持续同步？
• RQ4 在动态施工环境中，人机协同工作流程的性能如何？
• RQ5 闭环BIM更新在多大程度上提升了机器人施工任务的可靠性和准确性？

主要发现

• 所提出的框架成功使机械臂在实时现场变化条件下完成石膏板安装任务，展现出良好的适应性。
• 仅在12%的任务周期中需要人类干预，表明系统具备高度自主性与鲁棒性。
• 与静态BIM规划相比，数字孪生将BIM建模误差降低了89%，尤其在不确定条件下表现更优。
• 在实验室和仿真环境中，系统在任务完成率上达到了94%。
• 通过持续将实际建造数据更新至BIM，显著提升了下游规划的准确性和协调性。
• 人类监督与自适应机器人规划的结合，有效减少了返工，并提升了操作安全性。