Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-26T05:36:32.835Z Has data issue: false hasContentIssue false
  • English
  • Français

Fault detection and isolation in cooperative mobile robots using multilayer architecture and dynamic observers

Published online by Cambridge University Press:  12 August 2010

R. A. Carrasco ,
F. Núñez  and
A. Cipriano
R. A. Carrasco*
Affiliation:
Department of Industrial Engineering and Operations Research, Columbia University, New York, USA1
F. Núñez
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, PO Box 306, Santiago 6904411, Chile
A. Cipriano
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, PO Box 306, Santiago 6904411, Chile
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Mobile robot systems are being used more often in tasks that protect human operators from dangerous environments, but these benefits can be easily lost if the robots spend much of their time being repaired. This implies that any increment in their reliability will also improve their benefits. One way to achieve this is by adding redundant elements to the robot, but this adds complexity and cost to the design. On the other hand, cooperative mobile robots formed by members with the same basic structure provide a natural redundancy of elements, which may be used for reliability improvement. This work presents an architecture that takes advantage of the analytical and sensor redundancy present in groups of cooperative mobile robots in order to increase the reliability of the whole system. First, the design of the architecture is portrayed and the faults to be detected are described. The different layers of the system are then explained and analyzed using several simulations to test their capabilities and limitations. Finally, the experimental results on a group of small mobile robots are shown, validating the results delivered by simulations. These results show that the proposed architecture is able to detect and isolate correctly most of the faults tested.

Keywords

Cooperative systemsDistributed detectionFault detection and isolationMobile robots
Type
Articles
Information
Robotica , Volume 29 , Issue 4 , July 2011 , pp. 555 - 562
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Carlson, J., Murphy, R. R. and Nelson, A., “Follow-up Analysis of Mobile Robot Failures,” Proceedings of the 2004 IEEE International Conference on Robotics and Automation, New Orleans (2004) pp. 4987–4994.Google Scholar
2.Isermann, R. and Balle, P., “Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes,” Proceedings of the IFAC 13th Triennial World Congress, San Francisco, California (1996) vol. 7, pp. 1–12.Google Scholar
3.Basseville, M. and Nikiforov, I. V., Detection of Abrupt Changes—Theory and Application, 2nd ed., available at http://www.irisa.fr/sisthem/kniga/ (Nov. 1998).Google Scholar
4.Gertler, J., “Survey of model-based failure detection and isolation in complex plants,” IEEE Control Syst. Mag. 8 (6), 3–11 (1988).CrossRefGoogle Scholar
5.Willsky, A. S., “A survey of design methods for failure detection in dynamic systems,” Automatica 12, 601–611 (1976).CrossRefGoogle Scholar
6.Aravena, J. and Chowdhury, F., “Fault Detection of Flight Critical Systems,” Proceedings of the 20th Conference on Digital Avionics Systems, Daytona Beach (2001) pp. 1–10.Google Scholar
7.Napolitano, M., Windon, D., Casanova, J., Innocenti, M. and Silvestri, G., “Kalman filters and neural-network schemes for sensor validation in flight control systems,” IEEE Trans. Control Syst. Technol. 6 (5), 596–611 (1998).CrossRefGoogle Scholar
8.Qin, G., Ge, A. and Li, H., “On-board fault diagnosis of automated manual transmission control system,” IEEE Trans. Control Syst. Technol. 12 (4), 564–568 (2004).CrossRefGoogle Scholar
9.Brambilla, D., Capisani, L. M., Ferrara, A. and Pisu, P., “Fault detection for robot manipulators via second-order sliding modes,” IEEE Trans. Indust. Electron. 55 (11), 3954–3963 (2008).CrossRefGoogle Scholar
10.Maybeck, P. S. and Hanlon, P. D., “Multiple-model adaptive estimation using a residual correlation Kalman filter bank,” IEEE Trans. Aerosp. Electron. Syst. 36 (2), 393–406 (2000).Google Scholar
11.Isermann, R., “Model Based Fault Detection and Diagnosis Methods,” Proceedings of the American Control Conference, Seattle (1995), pp. 1605–1609.Google Scholar
12.Calado, J. M. F., Korbicz, J., Patan, K., Patton, R. and Sa Da Costa, J., “Soft computing approaches to fault diagnosis for dynamic systems,” Eur. J. Control 7, 248–286 (2001).CrossRefGoogle Scholar
13.Simon, D., Optimal State Estimation: Kalman, H∞and Nonlinear Approaches (John Wiley & Sons, Hoboken, New Jersey, 2006).CrossRefGoogle Scholar
14.Roumeliotis, S. I., Sukhatme, G. S. and Bekey, G. A., “Fault Detection and Identification in a Mobile Robot using Multiple-Model Estimation,” Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (1998) pp. 2223–2228.Google Scholar
15.Roumeliotis, S. I., Sukhatme, G. S. and Bekey, G. A., “Sensor Fault Detection and Identification in a Mobile Robot,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Victoria, Canada (1998), pp. 1383–1388.Google Scholar
16.Goel, P., Dedeoglu, G., Roumeliotis, S. I. and Sukhatme, G. S., “Fault Detection and Identification in a Mobile Robot Using Multiple Model Estimation and Neural Network,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, California (2000), pp. 2302–2309.Google Scholar
17.Washington, R., “On-board Real-Time State and Fault Identification for Rovers,” Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, California (2000), pp. 1175–1181.Google Scholar
18.Dixon, W. E., Walker, I. D. and Dawson, D. M., “Fault Detection for Wheeled Mobile Robots with Parametric Uncertainty,” Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Como, Italia (2001) pp. 1245–1250.Google Scholar
19.Yang, J. M., “Fault-tolerant crab gaits and turning gaits for a hexapod robot,” Robotica 24 (2), 269–270 (2006).CrossRefGoogle Scholar
20.Daniël Karssen, J. G. and Wisse, M., “Fall detection in walking robots by multi-way principal component analysis,” Robotica 27 (2), 249–257 (2009).CrossRefGoogle Scholar
21.Michaelson, D. G. and Jiang, J., “Modeling of redundancy in multiple mobile robots,” Proceedings of the American Control Conference, Chicago (2000), pp. 28–30.Google Scholar
22.Parker, L., “ALLIANCE: An architecture for fault tolerant multirobot cooperation,” IEEE Trans. Robot. Autom. 14 (2), 220–240 (1998).CrossRefGoogle Scholar
23.Tinós, R., Navarro-Serment, L. E. and Paredis, C. J. J., “Fault Tolerant Localization for Teams of Distributed Robots,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui (2001), pp. 1061–1066.Google Scholar
24.Tinós, R., Terra, M. H. and Bergerman, M., “Fault Tolerance in Cooperative Manipulators,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC (2002), pp. 470–475.Google Scholar
25.Heredia, G., Caballero, F., Maza, I., Merino, L., Viguria, A. and Ollero, A., “Multi-UAV Cooperative Fault Detection Employing Vision Based Relative Position Estimation,” Proceedings of the 17th IFAC World Congress, Seoul (2008), pp. 12093–12098.Google Scholar
26.Heredia, G., Caballero, F., Maza, I., Merino, L., Viguria, A. and Ollero, A., “Multi-unmanned aerial vehicle (UAV) cooperative fault detection employing differential global positioning (DGPS), inertial and vision sensors,” Sensors 9, 7566–7579 (2009).CrossRefGoogle ScholarPubMed
27.Visinsky, M. L., Walker, I. D. and Cavallaro, J. R., “Layered Dynamic Fault Detection and Tolerance for Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta (1993), vol. 2, pp. 180–187.Google Scholar
28.Visinsky, M. L., Cavallaro, J. R. and Walker, I. D., “A dynamic fault tolerance framework for remote robots,” IEEE Trans. Robot. Autom. 11 (4), 477–490 (1995).CrossRefGoogle Scholar