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Contact-State Recognition of Compliant Motion Robots Using Expectation Maximization-Based Gaussian Mixtures
Jasim, Ibrahim; Plapper, Peter
2014In Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014)
Peer reviewed
 

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Keywords :
Contact-State; Compliant Motion Robots; Force-Controlled Robots; Gaussian Mixtures; Robotic Assembly
Abstract :
[en] In this article, we address the problem of Contact-State (CS) recognition for force-controlled robotic tasks. At first, the wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals of the manipulated object, in different Contact Formations (CFs) of a task, are collected. Then in the framework of the Bayesian classification, the Expectation Maximization-based Gaussian Mixtures Model (EM-GMM) is used in building efficient CFs classifiers. The use of the EM-GMM in developing the captured signals models accommodates possible signals non-stationarity, i.e. signals abnormal distribution, and enhanced recognition performance would be resulted. Experiments are performed on a KUKA Lightweight Robot (LWR) doing the cube-in-corner assembly task, which is a rigid cube object interacting with an environment composed of three orthogonal planes, and different CFs are considered. From the experimental results, the EM-GMM is shown to have an excellent recognition performance with an enhanced computational time. In order to compare the EM-GMM with the available CF recognition schemes, we developed the corresponding CF classifiers using the Gravitational Search-Fuzzy Clustering Algorithm (GS-FCA), Stochastic Gradient Boosting (SGB), and the Conventional Fuzzy Classifier(CFC) approaches. From the comparison results it is obvious that the EM-GMM scheme is outperforming the rest.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Jasim, Ibrahim ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Plapper, Peter ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
External co-authors :
no
Language :
English
Title :
Contact-State Recognition of Compliant Motion Robots Using Expectation Maximization-Based Gaussian Mixtures
Publication date :
02 June 2014
Event name :
Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014)
Event date :
2-6-2014 to 3-6-2014
Audience :
International
Main work title :
Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014)
Publisher :
VDE
ISBN/EAN :
978-3-8007-3601-0
Peer reviewed :
Peer reviewed
FnR Project :
FNR2955286 - Self-adaptive Fuzzy Control For Robotic Peg-in-hole Assembly Process, 2011 (01/05/2012-30/04/2016) - Ibrahim Fahad Jasim Ghalyan
Name of the research project :
R-AGR-0071 - IRP13 - PROBE (20130101-20151231) - PLAPPER Peter
Available on ORBilu :
since 25 June 2014

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