Scalable robot fault detection and identification

Robotics and Autonomous Systems

Volumn 54, Issue 2, 2006, Pages 184-191

  Verma, Vandi ^a   Simmons, Reid ^b,c  

^a NASA AMES RESEARCH CENTER   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c Carnegie Mellon University   (United States)

Author keywords

Fault protection; Robot fault detection; Robot fault diagnosis

Indexed keywords

FAULT PROTECTION; ROBOT FAULT DETECTION; ROBOT FAULT DIAGNOSIS; SYSTEM DYNAMICS;

ADAPTIVE ALGORITHMS; APPROXIMATION THEORY; COMPUTER SIMULATION; LINEAR CONTROL SYSTEMS; MONTE CARLO METHODS; PROBABILITY; TRACKING (POSITION);

ROBOTS;

EID: 30944436833     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2005.09.028     Document Type: Conference Paper

References (23)

1. D. Avitzour A stochastic simulation bayesian approach to multitarget tracking IEEE Transaction on Aerospace and Electronic systems 1995
2. L. Campo, and Y. Bar-Shalom Control of discrete-time hybrid stochastic systems IEEE Transactions on Automatic Control 37 1988 1522 1527
3. Y. Bar-Shalom, and T.R. Fortmann Tracking and Data Association 1988 Academic Press
4. J.E. Bares, and D.S. Wettergreen Dante ii: Technical description, results and lessons learned International Journal of Robotics Research 18 July 1999 621 664
5. C. Bererton, P. Khosla, An analysis of cooperative repair capabilities in a team of robots, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2002
6. J. Carlson, R. Murphy, Reliability analysis of mobile robots, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2003
7. A. Doucet, D. Crisan, A survey of convergence results on particle filtering for practitioners, in: IEEE Trans. Signal Processing, 2002
8. A. Doucet N. de Freitas N. Gordon Sequential Monte Carlo Methods in Practice 2001 Springer-Verlag
9. D. Fox, W. Burgard, and S. Thrun Markov localization for mobile robots in dynamic environments Journal of Artificial Intelligence Research 11 1999 391 427
10. N.J. Gordon, D.J. Salmond, and A.F.M. Smith Novel approach to nonlinear/non gaussian bayesian state estimation IEE Proceedings-F 140 1993 107 113
11. M. Isard, and A. Blake CONDENSATION: conditional density propagation for visual tracking International Journal of Computer Vision 29 1 1998 5 28
12. R. Isermann, and P. Balle Trends in the application of model-based fault detection and diagnosis of technical processes Control Engineering Practice 1997
13. K. Kanazawa, D. Koller, S.J. Russel, Stochastic simulation algorithms for dynamic probabilistic networks, in: Proceedings of Uncertainly in Artificial Intelligence, 1995
14. G. Kitagawa, A monte carlo filtering and smoothing method for non-gaussian nonlinear state space models, in: Proceedings of the 2nd U.S. -Japan Joint Seminar on Statistical Time Series Analysis, 1993
15. P.C. Leger, Darwin2K: An Evolutionary Approach to Automated Design for Robotics, Kluwer Academic Publishers, 2000
16. N. Metropolis, and S. Ulam The monte carlo method Journal of the American Statistical Association 44 1949
17. NASA. Mars program independent assessment team summary report, Report published by investigation team, March 2000
18. D. Teneketzis, R.B. Washburn, T.G. Allen, Performance analysis for hybrid state estimation problems, in: Proceedings of the American Control Conference, 1985, pp. 1047-1053
19. D.B. Rubin, Using the SIR Algorithm to Simulate Posterior Distributions. Oxford University Press, 1988
20. R.Y. Rubinstein Simulation and Monte Carlo method 1981 John Wiley and Sons
21. S. Thrun, J. Langford, V. Verma, Risk sensitive particle filters, in: Nips, December 2001
22. V. Verma, J. Langford, R. Simmons, Non-parametric fault identification for space rovers, in: Isairas, June 2001
23. V. Verma, S. Thrun, R. Simmons, Variable resolution particle filter, in: International Joint Conference of Artificial Intelligence, 2003