Dexterous robotic hand grasping method for automotive parts

Kitting processes are fundamental enablers in flexible manufacturing environments derived from minomi principles. A typical kitting application will sort loose and unpacked parts without dunnage into a tray or “kit” and then place them near the point of assembly for easy reach by assembly workers. In order to prepare, sort and sequence the kits, it is necessary to have adaptable robots and automation able to pick-and-place a variety of parts at line production rate. This requires the assembling any of hundreds of kit types, each one having about 10 different parts within 60 seconds. These requirements are a fundamental challenge for kitting automation given that it requires several parts of different shapes, which are presented in either random or semi-structured fashion, to be grasped and placed into the kit. Highly flexible manufacturing (HFM) requires grasping previously unknown objects for which a computer 3D model may not be available. A methodology that integrates vision and flexible robotic grasping is proposed to address HFM herein. The proposed set of hand grasping shapes presented here is based on the capabilities and mechanical constraints of the robotic hand. Pre-grasp shapes for a Barrett Hand are studied and defined using finger spread and flexion. In addition, a simple and efficient vision algorithm is used to servo the robot and to select the pre-grasp shape in the pick-and-place task of 9 different vehicle door parts. Finally, experimental results evaluate the ability of the robotic hand to grasp both pliable and rigid parts.