Skip to main content
SpringerLink
Log in

Fault detection in autonomous robots based on fault injection and learning

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot’s sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Arlat, J., Aguera, M., Amat, L., Crouzet, Y., Fabre, J., Laprie, J., Martins, E., & Powell, D. (1990). Fault injection for dependability validation: a methodology and some applications. IEEE Transactions on Software Engineering, 16(2), 166–182.

    Article  Google Scholar 

  • Ashokaraj, I., Tsourdos, A., Silson, P., & White, B. A. (2004). Sensor based robot localisation and navigation: using interval analysis and unscented Kalman filter. In Proceedings of the 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS 2004) (Vol. 1, pp. 64–70). Las Vegas: IEEE Press.

    Chapter  Google Scholar 

  • Canham, R., Jackson, A., & Tyrrell, A. (2003). Robot error detection using an artificial immune system. In Proceedings of NASA/DoD conference on evolvable hardware (pp. 199–207). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Carlson, J., & Murphy, R. (2003). Reliability analysis of mobile robots. In Proceedings of IEEE international conference on robotics and automation, ICRA’03 (Vol. 1, pp. 274–281). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Clouse, D., Giles, C., Horne, B., & Cottrell, G. (1997). Time-delay neural networks: representation and induction of finite-state machines. IEEE Transactions on Neural Networks, 8, 1065–1070.

    Article  Google Scholar 

  • Dearden, R., Hutter, F., Simmons, R., Thrun, S., Verma, V., & Willeke, T. (2004). Real-time fault detection and situational awareness for rovers: report on the Mars technology program task. In Proceedings of IEEE aerospace conference (Vol. 2, pp. 826–840). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Dias, M. B., Zinck, M. B., Zlot, R. M., & Stentz, A. (2004). Robust multirobot coordination in dynamic environments. In Proceedings of IEEE conference on robotics and automation, ICRA’04 (Vol. 4, pp. 3435–3442). Piscataway: IEEE Press.

    Google Scholar 

  • Dorigo, M., Trianni, V., Şahin, E., Groß, R., Labella, T. H., Baldassarre, G., Nolfi, S., Deneubourg, J.-L., Mondada, F., Floreano, D., & Gambardella, L. M. (2004). Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots, 17(2–3), 223–245.

    Article  Google Scholar 

  • Forrest, S., Perelson, A., Allen, L., & Cherukuri, R. (1994). Self-nonself discrimination in a computer. In Proceedings of the 1994 IEEE symposium on research in security and privacy (Vol. 212, pp. 202–212). Los Alamitos: IEEE Computer Society.

    Google Scholar 

  • Gerkey, B., & Matarić, M. J. (2002a). Pusher-watcher: an approach to fault-tolerant tightly-coupled robot coordination. In Proceedings of IEEE international conference on robotics and automation, ICRA’02 (pp. 464–469). Piscataway: IEEE Press.

    Google Scholar 

  • Gerkey, B. P., & Matarić, M. J. (2002b). Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5), 758–768.

    Article  Google Scholar 

  • Gertler, J. J. (1988). Survey of model-based failure detection and isolation in complex plants. IEEE Control Systems Magazine, 8, 3–11.

    Article  Google Scholar 

  • Goel, P., Dedeoglu, G., Roumeliotis, S., & Sukhatme, G. (2000). Fault detection and identification in a mobile robot using multiple model estimation and neural network. In Proceedings of IEEE international conference on robotics and automation, ICRA’00 (Vol. 3, pp. 2302–2309). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Groß, R., Bonani, M., Mondada, F., & Dorigo, M. (2006). Autonomous self-assembly in swarm-bots. IEEE Transactions on Robotics, 22(6), 1115–1130.

    Article  Google Scholar 

  • Hinchey, M., Rash, J., Rouff, C., & Truszkowski, W. (2004). NASA’s swarm missions: the challenge of building autonomous software. IT Professional, 6, 47–52.

    Article  Google Scholar 

  • Hsueh, M., Tsai, T., & Iyer, R. (1997). Fault injection techniques and tools. Computer, 30(4), 75–82.

    Article  Google Scholar 

  • Isermann, R. (1997). Supervision, fault-detection and fault-diagnosis methods—an introduction. Control Engineering Practice, 5(5), 639–652.

    Article  Google Scholar 

  • Isermann, R., & Ballé, P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5), 709–719.

    Article  Google Scholar 

  • Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th international symposium on aerospace/defense sensing, simulation and controls (Vol. 3, pp. 182–193). Bellingham: SPIE.

    Google Scholar 

  • Kalman, R. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.

    Google Scholar 

  • Kochan, A. (2005). A bumper year for robots. Industrial Robot: An International Journal, 32, 201–204.

    Article  Google Scholar 

  • Leonard, J. J., & Durrant-Whyte, H. F. (1991). Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation, 7(3), 376–382.

    Article  Google Scholar 

  • Lerner, U., Parr, R., Koller, D., & Biswas, G. (2000). Bayesian fault detection and diagnosis in dynamic systems. In Proceedings of the 7th national conference on artificial intelligence (pp. 531–537). Cambridge: AAAI Press/MIT Press.

    Google Scholar 

  • Lewis, M. A., & Tan, K. H. (1997). High precision formation control of mobile robots using virtual structures. Autonomous Robots, 4(4), 387–403.

    Article  Google Scholar 

  • Li, P., & Kadirkamanathan, V. (2001). Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. IEEE Transactions Systems, Man Cybernetics, Part C, 31(3), 337–343.

    Article  Google Scholar 

  • Marsland, S., Nehmzow, U., & Shapiro, J. (2005). On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems, 51(2–3), 191–206.

    Google Scholar 

  • Mondada, F., Pettinaro, G. C., Guignard, A., Kwee, I., Floreano, D., Deneubourg, J.-L., Nolfi, S., Gambardella, L., & Dorigo, M. (2004). Swarm-bot: a new distributed robotic concept. Autonomous Robots, 17(2–3), 193–221.

    Article  Google Scholar 

  • Mondada, F., Gambardella, L. M., Floreano, D., Nolfi, S., Deneubourg, J.-L., & Dorigo, M. (2005). The cooperation of swarm-bots: physical interactions in collective robotics. IEEE Robots and Automation Magazine, 12(2), 21–28.

    Article  Google Scholar 

  • Nouyan, S., Groß, R., Bonani, M., Mondada, F., & Dorigo, M. (2006). Group transport along a robot chain in a self-organised robot colony. In T. Arai, R. Pfeifer, T. Balch, & H. Yokoi (Eds.), Intelligent autonomous systems (Vol. 9, pp. 433–442). Amsterdam: IOS Press.

    Google Scholar 

  • Nouyan, S., Campo, A., & Dorigo, M. (2008). Path formation in a robot swarm: self-orgenized strategies to find your way home. Swarm Intelligence, 1(2).

  • O’Grady, R., Groß, R., Mondada, F., Bonani, M., & Dorigo, M. (2005). Self-assembly on demand in a group of physical autonomous mobile robots navigating rough terrain. In M. S. Capcarrere, A. A. Freitas, P. J. Bentley, C. G. Johnson, & J. Timmis (Eds.), Lecture notes in artificial intelligence : Vol. 3630. Advances in artificial life: 8th European conference, ECAL 2005 (pp. 272–281). Berlin: Springer.

    Google Scholar 

  • Parker, L. E. (1998). ALLIANCE: an architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation, 14(2), 220–240.

    Article  Google Scholar 

  • Patton, R., Uppal, F., & Lopez-Toribio, C. (2000). Soft computing approaches to fault diagnosis for dynamic systems: a survey. In A. Edelmayer, C. Banyasz (Eds.), Proceedings of 4th IFAC symposium on fault detection supervision and safety for technical processes (Vol. 1, pp. 298–311). Oxford: Elsevier.

    Google Scholar 

  • Roumeliotis, S., Sukhatme, G., & Bekey, G. (1998). Sensor fault detection and identification in a mobile robot. In Proceedings of IEEE/RSJ international conference on intelligent robots and systems (Vol. 3, pp. 1383–1388). Los Alamitos: IEEE Computer Society Press.

    Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by back-propagating errors. Nature, 323, 533–536.

    Article  Google Scholar 

  • Skoundrianos, E. N., & Tzafestas, S. G. (2004). Finding fault—fault diagnosis on the wheels of a mobile robot using local model neural networks. IEEE Robotics and Automation Magazine, 11(3), 83–90.

    Article  Google Scholar 

  • Smith, R., & Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 5(4), 56.

    Article  Google Scholar 

  • Terra, M., & Tinos, R. (2001). Fault detection and isolation in robotic manipulators via neural networks: a comparison among three architectures for residual analysis. Journal of Robotic Systems, 18(7), 357–374.

    Article  MATH  Google Scholar 

  • Trianni, V., & Dorigo, M. (2006). Self-organisation and communication in groups of simulated and physical robots. Biological Cybernetics, 95, 213–231.

    Article  MATH  Google Scholar 

  • Verma, V., & Simmons, R. (2006). Scalable robot fault detection and identification. Robotics and Autonomous Systems, 54(2), 184–191.

    Article  Google Scholar 

  • Verma, V., Gordon, G., Simmons, R., & Thrun, S. (2004). Real-time fault diagnosis. IEEE Robotics and Automation Magazine, 11(2), 56–66.

    Article  Google Scholar 

  • Vemuri, A., & Polycarpou, M. (1997). Neural-network-based robust fault diagnosis in robotic systems. IEEE Transactions on Neural Networks, 8(6), 1410–1420.

    Article  Google Scholar 

  • Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions Acoustics, Speech, and Signal Processing, 37, 328–339.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, 50, Av. Franklin Roosevelt, CP 194/6, 1050, Brussels, Belgium

    Anders Lyhne Christensen, Rehan O’Grady, Mauro Birattari & Marco Dorigo

Authors
  1. Anders Lyhne Christensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rehan O’Grady
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauro Birattari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Dorigo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anders Lyhne Christensen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Christensen, A.L., O’Grady, R., Birattari, M. et al. Fault detection in autonomous robots based on fault injection and learning. Auton Robot 24, 49–67 (2008). https://doi.org/10.1007/s10514-007-9060-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-007-9060-9

Keywords

Search

Navigation