Reference : Contact-State Modelling in Force-Controlled Robotic Peg-in-Hole Assembly Processes of...
Scientific journals : Article
Engineering, computing & technology : Mechanical engineering
http://hdl.handle.net/10993/21892
Contact-State Modelling in Force-Controlled Robotic Peg-in-Hole Assembly Processes of Flexible Objects Using Optimised 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 >]
Voos, Holger mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
2015
Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Sage
Yes (verified by ORBilu)
International
0954-4054
2041-2975
[en] Expectation Maximisation ; Distribution Similarity Measure ; Flexible Objects Assembly ; Force-Controlled Robots ; Gaussian Mixture ; Peg-in-Hole
[en] This article proposes the distribution similarity measure–based Gaussian mixtures model for the contact-state (CS) modelling in force-guided robotic assembly processes of flexible rubber parts. The wrench (Cartesian force and torque) signals of the manipulated object are captured for different states of the given assembly process. The distribution similarity measure–based Gaussian mixtures model CS modelling scheme is employed in modelling the captured wrench signals for different CSs. The proposed distribution similarity measure–based Gaussian mixtures model CS modelling scheme uses the Gaussian mixtures model in modelling the captured signals. The parameters of the Gaussian mixtures models are computed using expectation maximisation. The optimal number of Gaussian mixtures model components for each CS model is determined by considering the classification success rate as an index for the similarity measure between the distribution of the captured signals and the developed models. The optimal number of Gaussian mixtures model components corresponds to the highest classification success rate; hence, object elasticity variation would be accommodated by properly choosing the optimal number of Gaussian mixtures model components. The performance of the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy is evaluated by a test stand composed of a KUKA lightweight robot doing peg-in-hole assembly processes for flexible rubber objects. Two rubber objects with different elasticity are considered for two experiments; in the first experiment, an elastic peg of 30 Shore A hardness is considered and that of the second experiment has hardness of 6 Shore A which is even softer than the one used in experiment 1. Employing the proposed distribution similarity measure–based Gaussian mixtures model CS modelling strategy excellent classification success rate was obtained for both experiments. However, more Gaussian mixtures model components are required for the softer one that gives a strong impression of the non-stationarity behaviour increment for softer materials. Comparison is performed with the available CS modelling schemes and the distribution similarity measure–based Gaussian mixtures model is shown to provide the best classification success rate performance with a reduced computational time.
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/21892
10.1177/0954405415598945
http://pib.sagepub.com/content/early/2015/09/04/0954405415598945
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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