Fixture-Free Automated Sewing System Using Dual-Arm Manipulator and High-Speed Fabric Edge Detection

Abstract

Inspired by human workers who perform complicated sewing tasks by repeating relatively simple operations, this paper proposes a fixture-free automated sewing system using a dual-arm manipulator and an ordinary sewing machine to sew two aligned fabrics along the edges, a common task in garment production. The proposed sewing system has a five-layer architecture: perception, dual-arm sewing Petri net, fundamental operations, control primitives, and hardware layers. This architecture decomposes various complex sewing tasks into sequences of fundamental operations. To meet the real-time requirement of automated sewing, a High-speed Fabric Edge Detection System (Hi-FEDS) is further proposed for the perception layer, which formulates the fabric edge detection problem for sewing as a classification problem of predefined distributed anchors. The anchor distribution is modeled by the Gaussian Uniform Mixture Model (GUMM). This method achieves high-speed fabric edge detection at an average of 120 FPS, with an average error of about one pixel. An experimental robotic sewing platform is developed, and the sewing results show that our system achieves high-quality sewing across fabrics of various shapes and materials.