Using Robust Regressions and Residual Analysis to Verify the Reliability of LS Estimation : Application in Robotics - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2009

Using Robust Regressions and Residual Analysis to Verify the Reliability of LS Estimation : Application in Robotics

Résumé

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.
Fichier non déposé

Dates et versions

hal-00402609 , version 1 (07-07-2009)

Identifiants

  • HAL Id : hal-00402609 , version 1

Citer

Alexandre Janot, Pierre Olivier Vandanjon, Maxime Gautier. Using Robust Regressions and Residual Analysis to Verify the Reliability of LS Estimation : Application in Robotics. 2009 IEEE/RSJ International Conference on Intelligent RObots and Systems, Oct 2009, France. 6p. ⟨hal-00402609⟩
377 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More