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Entanglements can cause robots to pick multiple parts within random bin picking applications. Previous approaches cope with this problem by shaking the gripped workpiece above the bin. However, these methods increase the cycle time and may decrease the robustness of the application. Therefore we propose a new method to separate entangled workpiece situations by using deep supervised learning. To generate annotated training data for a convolutional neural network we set up a simulation scene. In this scene, bins are filled with different amounts of sorted workpieces in several entangled situations. Each workpiece is then moved into different directions to path poses which are evenly distributed along the surface of a hemisphere. The emerging dataset consists of cropped depth images of entangled workpiece situations and several path poses. A serial connection of convolutional neural networks is trained on this dataset and proposes a sequence of poses yielding the general departure path. Finally, the performance of this method is validated on simulated data. To the best of our knowledge, our proposed method is the first systematic approach to find the best extraction strategy to separate entangled workpieces in a pile while decreasing the effective cycle time for gripping entangled workpieces and increasing the robustness significantly.

In random bin picking, grasps on a workpiece are often defined manually, which requires extensive time and expert knowledge. In this paper, we propose a method that generates and prioritizes grasps for vacuum and magnetic grippers by analyzing the CAD model of a workpiece and gripper geometry. Using projections of these models, heatmaps such as the overlap of gripper and workpiece, the center of gravity, and the surface smoothness are generated. To get a combined heatmap, which estimates the probability for a successful grip, all individual heatmaps are fused by means of a weighted sum. Grid-based sampling generates prioritized grasps and suggests the most suitable gripper automatically. This approach increases the autonomy of bin picking significantly.