On-line estimation of inertial parameters using a recursive total least-squares approach

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Volumn , Issue , 2008, Pages 3845-3852

  Kubus, Daniel ^a   Kröger, Torsten ^a   Wahl, Friedrich M ^a  

^a TECHNICAL UNIVERSITY OF BRAUNSCHWEIG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MATRICES; ESTIMATION EQUATIONS; FAST ESTIMATION; INERTIAL PARAMETERS; LEAST SQUARE; LEAST-SQUARES TECHNIQUES; ON-LINE ESTIMATION; POSE ESTIMATION; RECURSIVE INSTRUMENTAL VARIABLES; ROBOTICS APPLICATIONS; TOTAL LEAST SQUARES;

INTELLIGENT ROBOTS; OBJECT RECOGNITION; RECURSIVE FUNCTIONS;

PARAMETER ESTIMATION;

EID: 69549115124     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2008.4650672     Document Type: Conference Paper

References (24)

1. J. Gámez García, A. Robertsson, J. Gómez Ortega, and R. Johansson. Generalized contact force estimator for a robot manipulator. In Proc. of IEEE International Conference on Robotics and Automation, pages 4019-4024, 2006. (Pubitemid 44940682)
2. T. Kröger, D. Kubus, and F. M. Wahl. 6d force and acceleration sensor fusion for compliant manipulation control. In Proc. of IEEE/RSJ International Conference on Intellegent Robots and Systems, pages 2626-2631, 2006. (Pubitemid 46928293)
3. D. Kubus, T. Kröger, and F. M. Wahl. Improving force control performance by computational elimination of non-contact forces/torques. In Proc. of IEEE International Conference on Robotics and Automation, 2008.
4. D. Kubus, T. Kröger, and F. M. Wahl. On-line rigid object recognition and pose estimation based on inertial parameters. In Proc. of IEEE/RSJ International Conference on Intellegent Robots and Systems, 2007.
5. C.H. An, C. G. Atkeson, and J. M. Hollerbach. Model-based Control of a Robot Manipulator. The MIT Press series in artificial intelligence. The MIT Press, 1988.
6. K. Kozlowski. Modelling and Identification in Robotics. Advances in Industrial Control. Springer, 1998.
7. M. Niebergall and H. Hahn. Development of a measurement robot for identifying all inertia parameters of a rigid body in a single experiment. IEEE Transactions on Control Systems Technology, 9(2):416-423, March 2001.
8. A. Winkler and J. Suchy. Dynamic force/torque measurement using a 12dof sensor. In Proc. of IEEE/RSJ International Conference on Intellegent Robots and Systems, pages 1870-1875, 2007.
9. P. J. McKerrow. Introduction to Robotics. Addison-Wesley Publishers Ltd., 1991.
10. T. Kröger, D. Kubus, and F. M. Wahl. 12d force and acceleration sensing: A helpful experience report on sensor characteristics. In Proc. of IEEE International Conference on Robotics and Automation, 2008.
11. J. Swevers, C. Ganseman, D. B. Tükel, J. de Schulter, and H. Van Brussel. Optimal robot excitation and identification. IEEE Trans. on Robotics and Automation, 13(5):730-739, 1997.
12. J. Swevers, W. Verdonck, B. Naumer, S. Pieters, and E. Biber. An experimental robot load identification method for industrial application. The International Journal of Robotics Research, 21(8):701-712, 2002.
13. M. Gautier and W. Khalil. Exciting trajectories for the identifcation of base inertial parameters of robots. The International Journal of Robotics Research, 11(4):362-375, 1992. (Pubitemid 23567805)
14. D. J. Pierce and R. J. Plemmons. Tracking the condition number for RLS in signal processing. Mathematics of Control, Signals, and Systems, 5(1):23-39, 1992.
15. The MathWorks Inc. The mathworks homepage. http://www.mathworks.com/ products/optimization/functionlist.html [date: 02/15/2008]. Internet, 2008.
16. D. Kubus, T. Kröger, and F. M. Wahl. Estimating inertial load parameters using force/torque and acceleration sensor fusion. In VDIBericht Nr. 2012, 2008.
17. L. Ljung. System Identification: Theory for the User. Prentice Hall, 1999.
18. M. Brand. Incremental singular value decomposition of uncertain data with missing values. In Proceedings of the ECCV 2002, Lecture Notes on Computer Science 2350, 2002.
19. D. L. Boley and K. T. Sutherland. Recursive total least squares: An alternative to the discrete kalman filter. Technical Report TR 93-132, University of Minnesota, 1993.
20. J. L. Barlow and P. A. Yoon. Solving recursive tls problems using the rank-revealing ulv decomposition. In Proceedings of the second international workshop on recent advances in total least squares techniques and errors-in-variables modeling, pages 117-126, Philadelphia, PA, USA, 1997. Society for Industrial and Applied Mathematics.
21. M. Gu and S. C. Eisenstat. A stable and fast algorithm for updating the singular value decomposition. Technical report, Yale University, New Haven, 1994.
22. G. H. Golub and C. F. Van Loan. Matrix Computations. The Johns Hopkins University Press, 1990.
23. W. H. Press, S. A. Teukolski, W. T. Vetterling, and B. P. Flannery. Numerical Receipes in C++. Cambridge University Press, 2002.
24. B. Finkemeyer, T. Kröger, and F. M. Wahl. Executing assembly tasks specified by manipulation primitive nets. Advanced Robotics, 19(5):591-611, June 2005.