No abstract

No abstract

In this paper, we propose a robot end-effector for fabric folding along a straight line for garment production. In the garment production process, some of the edges of fabric parts of a garment need to be folded before sewing. A conventional automated folding system has a fixture designed for each shape and size of the folding part. The fixture is not universal. In the case of a pocket setter, for example, a pocket template of the fixture used for folding needs to be redesigned/reconfigured when the shape of the fabric part changes. A conventional automated fabric folding system is designed for the mass production of garments with the same shape and the same size. In this paper, we consider how to perform fabric folding without the use of a fixture, so that the same system could be used for folding fabric parts of different shapes and sizes. We propose a concept of a robot end-effector for fabric folding along a straight fold line and develop a prototype of an end-effector referred to as F-FOLD (Free-form FOLDing). Folding of the edge of a fabric part is achieved by moving F-FOLD along the desired straight fold line. Experimental results illustrate how F-FOLD folds a fabric part along a straight line.

<p>Visual servoing is one of the techniques to control robot pose based on captured images in real time. CNN-based visual servoing has been also proposed for positioning a robot end-effector with a hand-eye camera, a camera-mounted drone, and for positioning a rigid object grasped by a single manipulator. This paper proposes a new CNN-based visual servoing for positioning soft fabric parts by a dual manipulator system. The proposed control system aims to position a soft fabric part to the target pose while keeping the surface flat. Since the surface shape of the non-textured fabric part is difficult to recognize by a single grayscale image, we applied structured lighting to emphasize the features of the surface of the fabric part. Experiments demonstrate the fabric part positioning using a dual manipulator system and show that the fabric part can be positioned to the target pose with its surface kept flat.</p>

<p>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 manipulator’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.</p>