Collaborative Research: FW-HTF-RM: Human-in-the-Lead Construction Robotics: Future-Proofing Framing Craft Workers in Industrialized Construction

工业化建筑（IC）可以通过将许多任务转移到利用先进机器人技术的场外制造设施来解决建筑行业面临的紧迫的生产力和安全问题。然而，高度可定制的建筑设计、建筑材料固有的可变性以及劳动密集型的施工任务，使传统的自动化和机器人自动化的集成变得复杂。这项研究旨在通过引入一种革命性的人机协作方法来应对这些挑战，称为人类主导建筑机器人（HiLCR）。拟议中的HiLCR使手工艺工人能够在需要设计或精细运动控制的工业化任务中发挥“主导作用”，同时利用工业机器人手臂的基本物理能力发挥支持作用。HiLCR将确保先进的技术和机器作为工具，滋养而不是压制手工艺工人的创造力。HiLCR的这种平衡方法将帮助未来的工人克服迫在眉睫的机器失业危机，这是当前以人为本的自动化趋势造成的最大劳动力恐惧之一。此外，HiLCR可以通过将需要一定身体能力的体力工作（如手部灵活性和提升材料）委托给机器人系统来减少进入建筑行业的潜在物理障碍。本研究的总体目标是建立技术和社会基础，促进HiLCR在IC中的高效和平衡整合。为了实现这一目标，我们将我们的努力组织成两个主要的研究流：（1）工业化建筑中基于机器人的自动化改造;（2）工艺工人需求满意度和HiLCR参与度的评估。在第一个研究流程中，我们将分析板式结构的产品和生产过程，并为HiLCR开发直观的控制系统。产品分析将利用基于设计的建筑设计参数分析和建筑系统公差分析，为嵌板建筑开发基于装配的数据库。然后，生产分析将开发基于知识的系统，以标准化制造程序预先加载HiLCR。控制系统的技术发展将包括基于力的远程操作、触觉反馈系统和主动指挥中心。然后，拟议的人体工程学分析将确定可以修改标准化制造程序的区域，以安全地提高任务完成时间和团队绩效。在第二个研究流，我们将采用混合方法的研究，结合定性和定量数据收集和分析，重新评估的知识，技能，能力，以及在HiLCR应用的工艺工作成功的性能所需的其他因素;确定任务分配与更大的需求满意度和工艺工人的参与;该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Industrialized construction (IC) can address the pressing productivity and safety issues facing the construction industry by transferring many tasks to offsite manufacturing facilities that leverage the power of advanced robotic technologies. However, highly customizable building designs, the inherent variability in construction materials, and labor-intensive construction tasks complicate the integration of traditional craftwork and robotic automation. This research aims to address these challenges by introducing a revolutionary approach to human-robot collaboration, named Human-in-the-Lead Construction Robotics (HiLCR). The proposed HiLCR empowers craft workers to take the “lead” in industrialized tasks that require design-making or fine motor control, while leveraging the fundamental physical capacities of industrial robotic arms in a support role. HiLCR will ensure that advanced technologies and machines serve as tools that nourish the ingenuity of craft workers instead of suppressing it. This balanced approach of HiLCR will help future workers overcome the looming crisis of job loss to machines, one of the greatest workforce fears caused by the current trends in automating human-centric tasks. Furthermore, HiLCR can reduce potential physical barriers to entry into the construction industry by delegating manual work that requires certain physical abilities, such as hand dexterity and lifting of materials, to the robotic system.The overarching goal of this research is to establish the technical and social foundations that promote efficient and well-balanced integration of HiLCR in IC. To achieve this goal, we have organized our efforts into two primary research streams: (1) the transformation of robotics-based automation in industrialized construction; and (2) the evaluation of craft worker need satisfaction and engagement in HiLCR. In the first research stream, we will analyze the product and production processes of panelized construction and develop an intuitive control system for HiLCR. The product analysis will utilize design-based parametric analysis of building designs and tolerance analysis of construction systems to develop an assembly-based database for panelized construction. The production analysis will then develop knowledge-based systems to front-load HiLCR with standardized manufacturing procedures. The technological development of the control system will incorporate force-based teleoperation, haptic feedback systems, and active command centers. The proposed ergonomic analysis will then identify areas where standardized manufacturing procedures can be revised to safely improve task completion time and team performance. In the second research stream, we will employ mixed-method research that combines qualitative and quantitative data collection and analysis to re-evaluate the knowledge, skills, abilities, and other factors required for successful performance on craft work in HiLCR applications; identify task assignments associated with greater need satisfaction and engagement of craft workers; and develop curriculum to prepare new craft workers for working with and alongside robotic automation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.