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.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Gerkey, B. P., & Matarić, M. J. (2002b). Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5), 758–768.
Gertler, J. J. (1988). Survey of model-based failure detection and isolation in complex plants. IEEE Control Systems Magazine, 8, 3–11.
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.
Groß, R., Bonani, M., Mondada, F., & Dorigo, M. (2006). Autonomous self-assembly in swarm-bots. IEEE Transactions on Robotics, 22(6), 1115–1130.
Hinchey, M., Rash, J., Rouff, C., & Truszkowski, W. (2004). NASA’s swarm missions: the challenge of building autonomous software. IT Professional, 6, 47–52.
Hsueh, M., Tsai, T., & Iyer, R. (1997). Fault injection techniques and tools. Computer, 30(4), 75–82.
Isermann, R. (1997). Supervision, fault-detection and fault-diagnosis methods—an introduction. Control Engineering Practice, 5(5), 639–652.
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.
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.
Kalman, R. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.
Kochan, A. (2005). A bumper year for robots. Industrial Robot: An International Journal, 32, 201–204.
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.
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.
Lewis, M. A., & Tan, K. H. (1997). High precision formation control of mobile robots using virtual structures. Autonomous Robots, 4(4), 387–403.
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.
Marsland, S., Nehmzow, U., & Shapiro, J. (2005). On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems, 51(2–3), 191–206.
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.
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.
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.
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.
Parker, L. E. (1998). ALLIANCE: an architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation, 14(2), 220–240.
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.
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.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by back-propagating errors. Nature, 323, 533–536.
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.
Smith, R., & Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 5(4), 56.
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.
Trianni, V., & Dorigo, M. (2006). Self-organisation and communication in groups of simulated and physical robots. Biological Cybernetics, 95, 213–231.
Verma, V., & Simmons, R. (2006). Scalable robot fault detection and identification. Robotics and Autonomous Systems, 54(2), 184–191.
Verma, V., Gordon, G., Simmons, R., & Thrun, S. (2004). Real-time fault diagnosis. IEEE Robotics and Automation Magazine, 11(2), 56–66.
Vemuri, A., & Polycarpou, M. (1997). Neural-network-based robust fault diagnosis in robotic systems. IEEE Transactions on Neural Networks, 8(6), 1410–1420.
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.
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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
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DOI: https://doi.org/10.1007/s10514-007-9060-9