Adaptive Robotic Construction of Wood Frames

Abstract

Automated robotic construction of wood frames faces significant challenges, particularly in the perception of large studs and maintaining tight assembly tolerances amidst the natural variability and dimensional instability of wood. To address these challenges, we introduce a novel multi-modal, multi-stage perception strategy for adaptive robotic construction, particularly for wood light-frame assembly. Our strategy employs a coarse-to-fine method of perception by integrating deep learning-based stud pose estimation with subsequent stages of pose refinement, combining the flexibility of AI-based approaches with the precision of traditional computer vision techniques. We demonstrate this strategy through experimental validation and construction of two different wall designs, using both low- and high-quality framing lumber, and achieve far better precision than construction industry guidelines suggest for designs of similar dimension.