Khalid, Muhammad UsmanMuhammad UsmanKhalidSpenrath, FelixFelixSpenrathMönnig, ManuelManuelMönnigMoosmann, MariusMariusMoosmannBormann, RichardRichardBormannKunz, HolgerHolgerKunzHuber, MarcoMarcoHuber2022-03-143.9.20212021https://publica.fraunhofer.de/handle/publica/41143210.1007/978-3-662-62962-8_2910.24406/publica-r-411432In 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.enbin-pickingGreifenmaschinelles Sehenconference paper