Particle filtering for state estimation in industrial robotic systems

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 222, Issue 6, 2008, Pages 437-455

  Rigatos, G G ^a  

^a INDUSTRIAL SYSTEMS INSTITUTE   (Greece)

Author keywords

Extended Kalman filter; Gaussian filters; Industrial robotic manipulator; Non parametric filters; Particle filter; Sensor fusion; State estimation

Indexed keywords

ACOUSTIC NOISE; AIR FILTERS; CELLULAR RADIO SYSTEMS; CONTROL THEORY; ESTIMATION; EXTENDED KALMAN FILTERS; KALMAN FILTERS; NONLINEAR FILTERING; POSITION CONTROL; ROBOTICS; ROBOTS; SENSOR DATA FUSION; SIGNAL FILTERING AND PREDICTION; SPURIOUS SIGNAL NOISE; STATE ESTIMATION; TRELLIS CODES;

CASE STUDIES; END EFFECTORS; EXTENDED KALMAN FILTER; GAUSSIAN; GAUSSIAN FILTERS; INDUSTRIAL ROBOTIC MANIPULATOR; INDUSTRIAL ROBOTIC SYSTEMS; INDUSTRIAL SYSTEMS; MEASUREMENT NOISES; NON-PARAMETRIC; NON-PARAMETRIC FILTERS; PARTICLE FILTER; PARTICLE FILTERING; ROBOTIC MANIPULATORS; SENSOR FUSION; STATE VECTORS;

WAVE FILTERS;

EID: 52949144240     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/09596518JSCE463     Document Type: Article

References (40)

1. Kamen, E. W. and Su, J. K. Introduction to optimal estimation, 1999 (Springer, Berlin).
2. Rigatos, G. G. and Tzafestas, S. G. Extended Kalman filtering for fuzzy modelling and multi-sensor fusion. Mathematical and computer modelling of dynamical systems, vol. 13, no. 3, 2007 (Taylor & Francis, London).
3. Watanabe, K. and Tzafestas, S. G. Learning algorithms for neural networks with the Kalman filters. J. Intelligent Robotic Systems, 1990, 3, 305-319 (Springer).
4. Fichou, J., LeGland, F., and Mevel, L. Particle-based methods for parameter estimation and tracking: numerical experiments, Publication Interne IRISA, no. 1604, Rennes, France, 2004.
5. Ristic, B., Arulampalam, M. S., and Gordon, N. J. Beyond the Kalman filter: particle filters for tracking applications, 2004 (Artech House, Norwood, Massachusetts).
6. Gordon, N. J., Salmond, D. J., and Smith, A. F. M. A novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. Radar and Signal Processing, 1993, 140, 107-113.
7. Yang, N., Tian, W. F., Jin, Z. H., and Zhang, C. B. Particle filter for sensor fusion in a land vehicle navigation system. In Measurement science and technology, 2005, vol. 16, pp. 677-681 (Institute of Physics Publishing).
8. Thrun, S., Burgard, M., and Fox, D. Probabilistic robotics, 2005 (MIT Press, Cambridge, Massachusetts).
9. Caron, F., Davy, M., Duflos, E., and Vanheeghe, P. Particle filtering for multi-sensor data fusion with switching observation models: applications to land vehicle positioning. IEEE Trans. Signal Processing, 2007, 55(6), 2703-2719.
10. Gustafsson, F., Gunnarsson, F., Bergman, N., and Forssel, U. Particle filters for positioning, navigation and tracking. IEEE Trans. Signal Processing -Special Issue on Monte Carlo methods for statistical signal processing, 2005, 50(2), 425-437.
11. Li, P. and Kadirkamanathan, V. Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. IEEE Trans. SMC, Part C, 2001, 31(3), 337-343.
12. Kadirkamanathan, V., Jaward, M. H., Fabri, S. G., and Kadirkamanathan, M. Particle filters for recursive model selection in linear and nonlinear system identification. In Proceedings of the 39th IEEE CDC Conference, 2000, pp. 2391-2396.
13. Karlsson, R. and Norrlöf, M. Bayesian position estimation of an industrial robot using multiple sensors. In Proceedings of the IEEE International Conference on Control applications, Taipei, Taiwan, 2004, pp. 303-308.
14. Ng, G. W. Intelligent systems - fusion, tracking and control, 2003 (Research Studies Press Ltd).
15. Campillo, F., Cérou, F., LeGland, F., and Rakotozafy, R. Particle and cell approximations for nonlinear filtering, Rapport de Recherche INRIA, No. 2567, 1995.
16. Nethi, S., Pohjola, M., Eriksson, L., and Jänti, R. Simulation case studies of Wireless Networked Control Systems. In Proceedings of the second ACM Intelligent Conference and International Workshop on Modeling analysis and simulation of wireless and mobile systems, Chania, Crete, Greece, October 2007.
17. Lindhé, M., Johansson, K. H., and Bicchi, A. An experimental study of exploiting multi-path fading for robot communications. In Robotics: science and systems, Atlanta, Georgia, 2007.
18. Colandairaj, J., Scanlon, W., and Irwin, G. Understanding wireless networked control systems through simulation. J. Computing and Control Engng, 2005, 16(2), 26-31.
19. Schön, T. B., Törnqvist, D., and Gustafsson, F. Fast particle filters for multi-rate sensors. In EUSIPCO 2007 Conference, Proceedings of the 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September 2007.
20. Lewis, F. L., Jagannathan, S., and Yesildirek, A. Neural network control of robot manipulators and nonlinear systems, 1999 (Taylor & Francis, London).
21. Fu, K. S., Gonzalez, R. C., and Lee, G. S. G. Robotics: control, sensing, vision and intelligence, 1987 (McGraw-Hill, Maidenhead).
22. Rigatos, G. G., Tzafestas, C. S., and Tzafestas, S. G. Mobile robot motion control in partially unknown environments using a sliding-mode fuzzy-logic controller. In Robotics and autonomous systems, 2000 vol.33, pp. 1-11 (Elsevier, Oxford).
23. ∞ tracking of uncertain SISO nonlinear systems: an observerbased adaptive fuzzy approach. Int. J. Systems Science, 2007, 38(6), 459-472 (Taylor & Francis, London).
24. Tzafestas, S. G. and Rigatos, G. G. Neural and neuro-fuzzy FELA adaptive robot control using feedforward and counterpropagation networks. J. Intell. Robotic Systems, 1998, 23, 291-330 (Kluwer, Dordrecht).
25. Rigatos, G. G. and Tzafestas, S. G. Adaptive fuzzy control for the ship steering problem. J. Mechatronics, 2006, 16(6), 479-489 (Elsevier, Oxford).
26. Rigatos, G. G. Fuzzy stochastic automata for intelligent vehicle control. IEEE Trans. Ind. Electronics, 2003, 50(1), 76-79.
27. Jassemi-Zargani, R. and Necsulescu, D. Extended Kalman filter-based sensor fusion for operational space control of a robot arm. IEEE Trans. Instrum. Measemt, 2002, 51(6), 1279-1282.
28. Blauer, M. and Bélanger, P. R. State and parameter estimation for robotic manipulators using force measurements. IEEE Trans. Autom. Control, 1987, 32, 1055-1066.
29. Ahrens, J. H. and Khalil, H. K. Closed-loop behavior of a class of nonlinear systems under EKF-based control. IEEE Trans. Autom. Control, 2007, 52(3), 536-540.
30. Simandl, M. and Straka, O. Sampling densities of particle: a survey and comparison. In Proceedings of the 2007 American Control Conference ACC 2007, New York, July 2007, pp. 4437-4442.
31. Perez, P., Vermaak, J., and Blake, A. Data fusion for visual tracking with particles. Proc. IEEE, 2004, 92(3), 495-513.
32. Brémaud, P. Markov-chains, Gibbs fields, Monte-Carlo simulation and queues. In Texts in applied mathematics, 1999 (Springer, Berlin).
33. Doucet, A. De Freitas, N., and Gordon, N. Sequential Monte Carlo methods in practice, 2001 (Springer, Berlin).
34. Campillo, F., Particulate et Modeles de Markov Cachés, Master Course Notes on Filtrage et traitement des doneées, Université de Sud-Toulon Var, France, 2006.
35. Crisan, D. and Doucet, A. A survey of convergence results on particle filtering methods for practitioners. IEEE Trans. Signal Processing, 2002, 50(3), 736-746.
36. Arulampalam, S., Maskell, S. R., Gordon, N. J., and Clapp, T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing, 2002, 50, 174-188.
37. Kitagawa, G. Monte-Carlo filter and smoother for non-Gaussian non-linear state-space models. J. Computat Graph. Statist, 1996, 5(1), 1-25.
38. Bolić, M., Djurić, P. M., and Hong, H. Resampling algorithms and architectures for distributed particle filters, IEEE Trans. Signal Processing, 2005, 53(7), 2242-2450.
39. Míguez, J. Analysis of parallelizable resampling algorithms for particle filtering. Signal Processing, 2007, 87, pp. 3155-3174 (Elsevier, Oxford).
40. Luo, R. C. and Kay, M. G. Multisensor integration and fusion in intelligent systems. IEEE Trans. Systems, Man and Cybernetics, 1989, 19(3), 901-931.