Reference : Contact-State Recognition of Compliant Motion Robots Using Expectation Maximization-B...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/17045
Contact-State Recognition of Compliant Motion Robots Using Expectation Maximization-Based Gaussian Mixtures
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 >]
2-Jun-2014
Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014)
VDE
Yes
No
International
978-3-8007-3601-0
Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014)
2-6-2014 to 3-6-2014
[en] Contact-State ; Compliant Motion Robots ; Force-Controlled Robots ; Gaussian Mixtures ; Robotic Assembly
[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.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/17045
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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