Reference : Contact-state modeling of robotic assembly tasks using Gaussian mixture models
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/10993/29967
Contact-state modeling of robotic assembly tasks using Gaussian mixture models
English
Ibrahim, Jasim [University of Luxembourg]
Plapper, Peter mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2014
Procedia CIRP
Elsevier
23
229-234
Yes (verified by ORBilu)
2212-8271
The Netherlands
5th CATS 2014 - CIRP Conference on Assembly Technologies and Systems
13-11-2014 to 14-11-2014
Dresden
Germany
[en] Contact-State modeling ; force-controlled robots ; gaussian mixture models
[en] This article addresses the Contact-State (CS) modeling problem for the force-controlled robotic peg-in-hole assembly tasks. The wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals, of the manipulated object, are captured for different phases of the robotic assembly task. Those signals are utilized in building a CS model for each phase. Gaussian Mixture Models (GMM) is employed in building the likelihood of each signal and Expectation Maximization (EM) is used in finding the GMM parameters. Experiments are performed on a KUKA Lightweight Robot (LWR) doing camshaft caps assembly of an automotive powertrain. Comparisons are also performed with the available assembly modeling schemes, and the superiority of the EM-GMM scheme is shown with a reduced computational time.
R-AGR-0071 > PROBE > 01/01/2013 - 31/12/2015 > PLAPPER Peter
http://hdl.handle.net/10993/29967

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
2014-CIRP 2.pdfPublisher postprint465.11 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.