NDLI: Contact-state monitoring of force-guided robotic assembly tasks using expectation maximization-based Gaussian mixtures models

Content Provider	Springer Nature Link
Author	Jasim, Ibrahim F. Plapper, Peter W.
Copyright Year	2014
Abstract	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 nonstationary, 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 nonstationary 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 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.
Starting Page	623
Ending Page	633
Page Count	11
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	73
Issue Number	5-8
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2014-04-27
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Assembly monitoring Expectation maximization Gaussian mixtures Peg-in-hole Robotic assembly Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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