Reference : Gaussian Filtering for Enhanced Impedance Parameters Identification in Robotic Assemb...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Mechanical engineering
	To cite this reference:	http://hdl.handle.net/10993/21967

Title :	Gaussian Filtering for Enhanced Impedance Parameters Identification in Robotic Assembly Processes
Language :	English
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 >]
	Voos, Holger [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Publication date :	8-Sep-2015
Main document title :	20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), Luxembourg 8-11 September 2015
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015)
Event date :	8-9-2015 to 11-9-2015
Event organizer :	IEEE
Event place (city) :	Luxembourg
Event country :	Luxembourg
Keywords :	[en] Gaussian filter ; Impedance parameters ; Robotic assembly ; Signals smoothing
Abstract :	[en] Robot interaction with the environment is normally described as a mass-spring-damping impedance model and the estimation of such interaction impedance parameters requires the computation of the joint (or task) space velocity and acceleration. In many cases, the velocity and acceleration are computed by numerically computing the first and second derivatives of the sensed position signal. The numerical differentiation results in approximation errors when computing the velocity and acceleration signals that would have a direct impact on the estimation of the impedance parameters. This article proposes enhancing the estimation of the impedance parameters by smoothing the velocity and acceleration signals prior to the considered estimation process. Gaussian Smoothing Filter (GSF) is employed in smoothing the considered signals. After the smoothing process, impedance parameters estimation becomes more feasible using the available strategies like the Least Mean Square (LMS) or any other estimation approach. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing the assembly of the air-intake manifold of an automotive powertrain. The impedance parameters are estimated for the smoothed and unsmoothed cases in order to show the enhancement in the estimation process.
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	R-AGR-0071 > PROBE > 01/01/2013 - 31/12/2015 > PLAPPER Peter
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/21967
FnR project :	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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