Automating the sewing process presents significant challenges due to the inherent softness of fabrics and the limited control capabilities of sewing systems. To realize sewing automation, we propose a time-scaling modeling and control architecture of the robotic sewing system. By using the time-scaling modeling, the nonholonomic kinematics of the sewing process of the industrial sewing machine is linearized precisely. Based on this model, a two-layer real-time control architecture is proposed. The upper layer controls the sewn seam line trajectory using the model-based feedback control implemented in the time-scaling domain, while the lower layer controls the manipulator and the sewing machine using geometric-based trajectory generation and coordinated motion control of the robot and the sewing system in the time domain. The experimental results demonstrate that the sewing trajectories exponentially converge to the desired trajectories without overshooting under different initial conditions and sewing speeds. Besides, the same sewing trajectories under different sewing speeds are obtained for a given stitch size. The sewing results show the good performance and application potential of the proposed robotic sewing system.

This paper proposes a fixture-free 2D sewing system using a dual-arm manipulator, i.e., the seam lines of the top and bottom fabric parts are the same. The proposed 2D sewing system sews two stacked fabric parts together along a desired seam line printed on the top fabric part without the use of a fixture. In the proposed system, the set of aligned and stacked fabric parts is held by the end-effectors of the dual-arm manipulator in coordination. The dual-arm manipulator controls the motion of the fabric parts on the flat sewing table stitch by stitch in coordination, while keeping the manipulated fabric parts flat using the internal force applied to the set of fabric parts. A novel vision-based seam line tracking control is proposed to control the motion of the set of fabric parts along the printed seam line on the top fabric part. The convergence of the tracking error is analyzed for sewing along both straight and curved seam lines and is shown to be specified by the control parameters. Sewing experiments show that the tracking error converges to zero as analyzed. The sewing experiments also show that the newly proposed trajectory generation method, which synchronizes the coordinated motion of the manipulators and the motion of the sewing needle, is essential for achieving accurate sewing.

This paper proposes CNN-based visual servoing for simultaneous positioning and flattening of a soft fabric part placed on a table by a dual manipulator system. We propose a network for multimodal data processing of grayscale images captured by a camera and force/torque applied to force sensors. The training dataset is collected by moving the real manipulators, which enables the network to map the captured images and force/torque to the manipulatorâ\euro™s motion in Cartesian space. We apply structured lighting to emphasize the features of the surface of the fabric part since the surface shape of the non-textured fabric part is difficult to recognize by a single grayscale image. Through experiments, we show that the fabric part with unseen wrinkles can be positioned and flattened by the proposed visual servoing scheme.  *The complete version of this preprint paper is in proceedings of ICRA 2023. Please refer to “https://ieeexplore.ieee.org/document/10160635”.Â