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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Automatic Grasp Generation for Vacuum Grippers for Random Bin Picking
verfasst von : Muhammad Usman Khalid, Felix Spenrath, Manuel Mönnig, Marius Moosmann, Richard Bormann, Holger Kunz, Marco F. Huber
Erschienen in: Advances in Automotive Production Technology – Theory and Application
Verlag: Springer Berlin Heidelberg
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Abstract
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.