Reference : Contact-State Monitoring of Force-Guided Robotic Assembly Tasks Using Expectation Max...
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/10993/17046
Contact-State Monitoring of Force-Guided Robotic Assembly Tasks Using Expectation Maximization-based Gaussian Mixtures Models
English
Jasim, Ibrahim mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Plapper, Peter mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2014
International Journal of Advanced Manufacturing Technology
Springer Science & Business Media B.V.
73
5
623-633
Yes (verified by ORBilu)
International
0268-3768
[en] Assembly Monitoring ; Expectation Maximization ; Gaussian Mixtures ; Peg-in-Hole ; Robotic Assembly
[en] This article addresses the problem of Contact-State (CS) monitoring for peg-in-hole force-controlled robotic assembly tasks. In order to perform such a monitoring target, the wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals of the manipulated object are firstly captured for different CS's of the object (peg) with respect to the environment including the hole. The captured signals are employed in building a model (a recognizer) for each CS and in the framework of pattern classification the CS monitoring would be addressed. It will be shown that the captured signals are non-stationary, i.e. they have non-normal distribution that would result in performance degradation if using the available monitoring approaches. In this article, the concept of the Gaussian Mixtures Models (GMM) is used in building the likelihood of each signal and the Expectation Maximization (EM) algorithm is employed in finding the GMM parameters. The use of the GMM would accommodate the signals non-stationary behavior and the EM algorithm would guarantee the estimation of the optimal parameters set of the GMM for each signal and hence the modeling accuracy would be significantly enhanced. In order to see the performance of the suggested CS monitoring scheme, we installed a test stand that is composed of a KUKA Lightweight Robot (LWR) doing a typical peg-in-hole tasks. Two experiments are considered; in the first experiment we use the EM-GMM in monitoring a typical peg-in-hole robotic assembly process and in the second experiment we consider the robotic assembly of camshaft caps assembly of an automotive powertrain and use the EM-GMM in monitoring its CS's. For both experiments, the excellent monitoring performance will be shown. Furthermore, we compare the performance of the EM-GMM with that obtained when using available CS monitoring approaches. Classification Success Rate (CSR) and computational time will be considered as comparison indices and the EM-GMM will be shown to have a superior CSR performance with reduced a computational time.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/17046
10.1007/s00170-014-5803-x
FnR ; FNR2955286 > Ibrahim Jasim > > Self-adaptive Fuzzy Control for Robotic Peg-in-Hole Assembly Process > 01/05/2012 > 30/04/2016 > 2012

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