Model-Free Grasp Planning for Configurable Vacuum Grippers

Abstract

A concept consisting of a new configurable vacuum gripper system and a corresponding method for determining optimal grasp configurations solely based on 3D vision is introduced. The robot system consists of a dynamically configurable vacuum gripper, a visual sensor, and a robot arm that are used in combination with a new grasp planner to robustly grasp unknown objects in arbitrary positions. For this purpose, formalized aspects of selecting contact surfaces for arbitrary suction cups are described; the concept involves visual detection of the objects, segmentation, iterative grasp planning, and action execution. The approach allows for a fast and efficient, yet precise execution of grasps. The core idea is a two-step 3D data acquisition approach and grasp point computation that takes advantage of the fact that the suction cups of the gripper can all be aligned axis-parallel. Therefore, an adequate sensor-based surface acquisition is done from a single viewpoint with respect to the gripper. Results of realworld experiments show that the proposed concept is suitable for a wide range of different and unknown objects in our setup.