Bayesian position estimation of an industrial robot using multiple sensors

Proceedings of the IEEE International Conference on Control Applications

Volumn 1, Issue , 2004, Pages 303-308

  Karlsson, Rickard ^a   Norrlöf, Mikael ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CLOSED LOOP CONTROL SYSTEMS; DEGREES OF FREEDOM (MECHANICS); ITERATIVE METHODS; KALMAN FILTERING; LINEARIZATION; MATHEMATICAL MODELS; MOTION ESTIMATION; POSITION CONTROL; REAL TIME SYSTEMS; SENSORS;

BAYESIAN ESTIMATION; EXTENDED KALMAN FILTERS (EKF); ITERATIVE LEARNING CONTROL (ILC); PARTICLE FILTERS (PF);

INDUSTRIAL ROBOTS;

EID: 17744375226     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. B.D.O Anderson and J.B Moore. Optimal Filtering. Prentice Hall, Englewood Cliffs, NJ, 1979.
2. S. Arimoto, S. Kawamura, and F. Miyazaki. Bettering operation of robots by learning. Journal of Robotic Systems, 1(2):123-140, 1984.
3. N. Bergman. Recursive Bayesian Estimation: Navigation and Tracking Applications. PhD thesis, Linköping University, 1999. Dissertations No. 579.
4. P.I. Corke. A robotics toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3(1):24-32, March 1996.
5. Inc. Crossbow Technology. Accelerometers, High Sensitivity, LF Series, CXL02LF3. Web. http://www.xbow.com, Jan 2004.
6. A. Doucet, N. de Freitas, and N. Gordon, editors. Sequential Monte Carlo Methods in Practice. Springer Verlag, 2001.
7. Leica Geosystems. Laser trackers. Website, http://www.leica-geosystems. com/ims/product/ltd.htm, Jan 2004.
8. N.J. Gordon, D.J. Salmond, and A.F.M. Smith. A novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE Proceedings on Radar and Signal Processing, volume 140, pages 107-113, 1993.
9. S. Gunnarsson and M. Norrlöf. Iterative learning control of a flexible mechanical system using accelerometers. In Preprints of the 6th IFAC symposium on robot control, Vienna, Austria, Sep 2000.
10. S. Gunnarsson, M. Norrlöf, G. Hovland, U. Carlsson, T. Brogårdh, T. Svensson, and S. Moberg. Pathcorrection for an industrial robot. European Patent Application No. EP1274546, April 2001.
11. Svante Gunnarsson and Mikael Norrlöf. Iterative learning control of a flexible robot arm using accelerometers. In IEEE Conference on Control Applications, Taipei, Taiwan, Sep 2004.
12. A.H. Jazwinski. Stochastic processes and filtering theory, volume 64 of Mathematics in Science and Engineering. Academic Press, 1970.
13. R. E. Kalman. A new approach to linear filtering and prediction problems. Trans. AMSE, J. Basic Engineering, 82:35-45, 1960.
14. R. Karlsson. Simulation Based Methods for Target Tracking. Licentiate Thesis no. 930, Department of Electrical Engineering, Linköping University, Sweden, Feb 2002.
15. K. L. Moore. Iterative Learning Control for Deterministic Systems. Advances in Industrial Control. Springer-Verlag, London, 1993.
16. M. Norrlöf. Iterative Learing Control. Analysis, design and experiments. PhD thesis, Linköping University, 2000. Dissertations No. 653.
17. M. Norrlöf. An adaptive iterative learning control algorithm with experiments on an industrial robot. IEEE Transactions on Robotics and Automation, 18(2):245-251, April 2002.
18. M. Norrlöf and S. Gunnarsson. Experimental comparison of some classical iterative learning control algorithms. IEEE Transactions on Robotics and Automation, 18(4):636-641, August 2002.
19. L. Sciavicco and B. Siciliano. Modelling and Control of Robot Manipulators. Springer, 2000.
20. M. Östring, Svante Gunnarsson, and Mikael Norrlöf. Closed loop identification of an industrial robot containing flexibilities. Control Engineering Practice, 11(3):291-300, Mar 2003.
21. C. Svensson. Multivariable control of industrial robot. Masters Thesis LiTH-ISY-EX-3506-2004, Department of Electrical Engineering, Linköping University, 2004. In Swedish.