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.
Automatic Grasp Generation for Vacuum Grippers for Random Bin Picking
ARENA2036
Advances in Automotive Production Technology – Theory and Application ; Kapitel : 29 ; 247-255
2021-06-02
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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