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Vacuum gripper for collar: a) construction of the standard grippers, b) special construction solutions
Source publication
Applying vacuum grippers largely found its application in the catching and transfer the materials in all industries. Only in the textile and garment industry, because of the materials porosity is not widely applied. Therefore, we initiate the research of enhancing the use of vacuum gripper for textile materials manipulation. Tests have shown that t...
Context in source publication
Context 1
... or less permeable materials such as plastics, impregnated materials, tightly woven polyester, paper, labels, etc. and workpieces of multiple layers after thermoplastic adhesive (called a head-fixation), etc. can be transferred by vacuum grippers. Because of the need to achieve a vacuum (the problem of leakage of material) gripper should cover as large an area of the workpiece in order to achieve a force sufficient to hold it, and it is necessary to design grippers (form) for a specific workpiece, Fig 1. [3] ...
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Citations
It is a global challenge in the textile and apparel industry to grasp and separate fabric pieces automatically using mechanical devices. This paper summarized studies on grasping a textile cutting piece by different principles and mechanical systems, and focused on bionic soft fingers made of silica gel. In the study, we first tested single-point grasping to explore the factors that influence the grasping effects of soft fingers, and found that (a) the grasping margin is a crucial factor that influences the effect of grasping, (b) the sides and the directions of a piece play important roles in grasping, and a reverse side and a vertical direction often bring better results of grasping, and (c) although the opening distance of a soft finger is significant to the result of grasping, successful grasping is a joint result of the grasping margin and the opening distance. We then experimented with the arrangement of soft fingers, and discovered that (a) the shape and the area of a cut piece are the determinants for the number of soft fingers that have to be used, (b) a soft finger is needed at the intersections of a piece to guarantee unfolded grasping and transferring, and (c) the number of soft fingers to be used for a specific grasping task can be estimated after major factors are determined. The conclusion we proposed is easy to understand and is convenient for training or application in an industrial production. In the future, it is expected to be applied to the intelligent production of clothing.
In order to progress the research on automatic separation of fabric pieces from a cutting pile during the garment manufacture, a summary of the methods of grasping was made. Next, the process of cut garment pieces grasping by soft fingers was defined, and the mechanical model was analyzed in detail. After that a grasping experiment by soft fingers was carried out. The experiment results show that the parameters of a soft finger (positive pressure, opening distance) and the parameters of the fabric (grams per square meter, thickness, friction coefficient, stiffness) have great influence on the grasping effect. Furthermore, a grasping model was proposed. Finally, a test experiment was implemented to verify the applicability of the model proposed. This paper mainly contributed a bridge between the parameters of fabrics and the parameters of soft fingers which can help greatly to select or to design a suitable soft finger according to the properties of a fabric accurately and quickly. In addition, the grasping model of fabric pieces of garment by using robotic soft fingers was found to have a good potential applicability in the field of smart manufacture of garment in the near future.
The reliability of picking task for various objects in clutter, as measured on the Amazon Picking Challenge, is far from the expectations of automation companies. Even if the best-performed team, who run object detection before picking the object, had picked a wrong object in the competition. In this paper, we propose a practical method to compose a highly reliable picking system with verification-based approach to reduce the rate of wrong picking and raise the reliability of picking ordered objects. In our approach, which we call pick-and-verify, the robot recognizes object twice: in clutter scene to detect the target and in hand after picking an object with less time loss and rise of reliability of picking the target. For grasping the detected object we do not assume its pose and it is actually the target object, instead, we adopt vision-based grasp planning for vacuum gripper with sensed 3-D point cloud. With the presented approach, the reliability of picking target objects raised 50%, and the score in the APC2015 competition has been improved to be close to the best-performed team by picking 9 out of 12 objects in 10 min with the same hardware in our previous system.
