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Automatic Grasp Generation for Vacuum Grippers for Random Bin Picking

 
: Khalid, Muhammad Usman; Spenrath, Felix; Mönnig, Manuel; Moosmann, Marius; Bormann, Richard; Kunz, Holger; Huber, Marco

:
Postprint urn:nbn:de:0011-n-6364380 (1.1 MByte PDF)
MD5 Fingerprint: 878907a594beef1beaf5cc968abaa0e7
Erstellt am: 3.9.2021


Weißgraeber, Philipp:
Advances in Automotive Production Technology - Theory and Application : Stuttgart Conference on Automotive Production (SCAP 2020), 9th and 10th of November 2020
Wiesbaden: Springer Vieweg, 2021 (ARENA2036)
ISBN: 978-3-662-62961-1 (Print)
ISBN: 978-3-662-62962-8 (Online)
S.247-255
Stuttgart Conference on the Automotive Production (SCAP) <1, 2020, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
bin-picking; Greifen; maschinelles Sehen

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.

: http://publica.fraunhofer.de/dokumente/N-636438.html