A vision-guided adaptive and optimized robotic fabric gripping system for garment manufacturing automation

Abstract

Automating fabric manipulation in garment manufacturing remains a challenging task due to the characteristics of limp sheet materials and the diversity of fabrics used. This paper introduces an adaptive and optimized robotic fabric handling system, designed to address these challenges. The system comprises an industrial robot, four needle grippers, and a novel adaptive gripper jig system capable of adjusting the positions of the grippers adaptively to accommodate the shape and material properties of the garment fabric parts. To do this, an in-depth analysis of fabric gripping characteristics—accounting for material properties, gripping position, and fabric deformation—is conducted. A two-stage machine learning model predicting fabric deflection and folding is established from the analyzed data. This model is then incorporated into a vision-guided algorithm that determines the optimal gripping points on garment parts using corresponding CAD data. In addition, the exact position of the target fabric part is swiftly recognized via an algorithm that maps the real-time captured images to the CAD-based shape information. The decision-making information—namely optimal gripping points and garment part position—are subsequently transmitted to the robotic system for automated fabric handling process. The performance of the developed algorithms was quantitatively evaluated, and the integrated robotic system verified to be capable of completing garment manufacturing automation by connecting the processes of automatic fabric cutting and sewing.