Title
Using robust regressions and residual analysis to verify the reliability of LS estimation: application in robotics
Abstract
Usually, the identification of the dynamic parameters of robot makes use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories. This allows using linear least squares (LS) techniques to estimate the parameters. The efficiency of this method has been proved through experimental identifications of a lot of prototypes and industrial robots. However, it is known that LS estimators are sensitive to outliers and leverage points. Thus, it may be helpful to verify their reliability. This is possible by using robust regressions and residual analysis. Then, we compare the results with those obtained with classical LS regression. This paper deals with this issue and introduces the experimental identification and residual analysis of an one degree of freedom (DOF) haptic interface using the Huber's estimator. To verify the pertinence of our analyses, this comparison is also performed on a medical interface consisting of a complex mechanical structure.
Year
DOI
Venue
2009
10.1109/IROS.2009.5354469
St. Louis, MO
Keywords
Field
DocType
industrial robot,classical ls regression,ls estimator,dynamic parameter,inverse dynamic model,experimental identification,haptic interface,medical interface,ls estimation,robust regression,complex mechanical structure,residual analysis,robots,estimation,robotics,vectors,robust control,estimation theory,inverse dynamics,torque,degree of freedom,robustness,regression analysis,parameter estimation,filtering
Residual,Degrees of freedom (statistics),Computer science,Control theory,Outlier,Robustness (computer science),Control engineering,Estimation theory,Robust control,Linear least squares,Estimator
Conference
ISBN
Citations 
PageRank 
978-1-4244-3804-4
0
0.34
References 
Authors
8
3
Name
Order
Citations
PageRank
Alexandre Janot18612.37
P. O. Vandanjon2263.23
Maxime Gautier347776.28