Efficient Bin-Picking and Grasp Planning Based on Depth Data

The problem of object localization is a well-known problem in industrial robotics. Manufactured parts arrive at factories as bulk goods in boxes. Single parts need to be picked out of the boxes and have to be fed to a machine. The task of automatically isolating single objects is known as the bin-picking problem. Even in modern factories the task of bin-picking is not automated widely yet. The automatization of this task is expensive since state-of-the-art solutions require object-class specific algorithms. In this paper we present an applicable solution for the bin-picking problem which is based on a standard 3d-sensor and is able to handle arbitrary objects. Furthermore, it is robust against noise and object occlusions. Additionally, we propose an approach for optimal grasp pose estimation with collision avoidance that effectively reduces system cycle times.