FMSG: ARM4MOD: AI-powered and Robot-assisted Manufacturing for Modular Construction

Modular construction is a revolutionary way to transform the construction industry with established records of accelerating projects and reducing costs as compared to the traditional processes. However, new construction capabilities are needed to perform modular construction at scale, where the industry suffers from the dependency on skilled labors, which is a well-acknowledged challenge at manufacturing factories as well.

This project focuses on the facts that (a) every project is unique and necessitates efficiency and accuracy in recognition and handling workpieces, (b) design and production line changes are common, and necessitate design standardization and optimization of modules, and (c) production lines are complex in space and time, and necessitate the guidance of workers while processing design and installation information accurately.

This project is a unique attempt in studying modular construction within the context of Future Manufacturing (FM). It exploits opportunities at the intersection of AI/robotics/building information modeling and manufacturing, with the potential to increase the scalability of modular construction. This research will pioneer initial formulations to enable (a) high throughput in manufacturing through the definition and evaluation of processes that embrace real-time workpiece semantic grounding and in-situ AR-robotic assistance, (b) feasibility studies of optimizing and standardizing the design of modules, and utilization of a cyberinfrastructure for their standardization, (c) prototyping cyberinfrastructures as both novel ways of forming academia and industry partnerships, and data infrastructures to accelerate data-driven adaption in FM for modular construction, and (d) synergistic activities with a two-year institution to train and educate FM workforce for the potential of FM and technologies evaluated.

While the evaluations of technologies will focus on the modular construction, the proposed technologies will improve the competitiveness of manufacturing industries, particularly heavy manufacturing industries that share similar challenges such as agricultural, mining, and ship building. The project will enhance the US competitiveness in production, bolster economic growth, educate students, and influence workforce behavior towards efficiency and accuracy with the skills required for leadership in FM.

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.