This paper describes a method to pick up a folded cloth product by a single-armed robot. We focus on a problem on picking up a folded cloth, and organize tasks to attack it. Then, we propose methods of grasp position estimation composed of two stages: detection of the thickest folded hem and pose estimation of the cloth product. In addition, we attempt to search for appropriate grasping postures, and show that there are regions where the success rate of grasp was high. In experiments using an actual robot, we achieved a picking task with 92% success rate.
This paper describes a robotic application to picking up a folded cloth by an articulated manipulator. It is assumed that an item of cloth is placed on a table, and our task is to pick up the cloth without being unfolding. We give a fundamental grasping point on a cloth item in advance. Meanwhile, the manipulator robot must estimate the pose of the cloth item, determine a grasping point, and plan grasping pose. We proposed methods of graspable hem detection, and grasping pose detection. Experimental results show the effectiveness of the proposed methods.
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