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Improving Robustness of Mobile Robots Using Model-based Reasoning

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Abstract

Retaining functionality of a mobile robot in the presence of faults is of particular interest in autonomous robotics. From our experiences in robotics we know that hardware is one of the weak points in mobile robots. In this paper we present the foundations of a system that automatically monitors the driving device of a mobile robot. In case of a detected fault, e.g., a broken motor, the system automatically reconfigures the robot in order to still allow to reach a certain position. The described system is based on a generalized model of the motion hardware. High-level control like path-planner only to change its behavior in case of a serious damage. The high-level control system remains the same. In the paper we present the model and the foundations of the diagnosis and reconfiguration system.

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References

  1. Carlson, J., Murphy, R.R.: How UGVs physically fail in the field. IEEE Transactions on Robotics 21(3), 423–437 (2005)

    Article  Google Scholar 

  2. Steinbauer, G., Brandstötter, M., Buchleitner, M., Galler, S., Jantscher, S., Krammer, G., Mörth, M., Weber, J., Weiglhofer, M.: Mostly harmless team description 2006 – robust control of mobile robots. In: Proceedings of the International RoboCup Symposium, Bremen, Germany (2006)

  3. Murphy, R.R.: Introduction to AI Robotics. MIT Press, Cambridge, MA (2002)

    Google Scholar 

  4. Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile Robots. MIT Press, Cambridge, MA (2004)

    Google Scholar 

  5. Hofbaur, M.W., Williams, B.C.: Mode estimation of probabilistic hybrid systems. In: Tomlin, C.J., Greenstreet, M.R. (eds.) Hybrid Systems: Computation and Control, HSCC, volume 2289 of Lecture Notes in Computer Science, pp. 253–266. Springer, Berlin Heidelberg New York (2002)

    Google Scholar 

  6. Hofbaur, M.W.: Hybrid estimation of complex systems. In: Lecture Notes in Control and Information Sciences, vol. 319. Springer, Berlin Heidelberg New York (2005)

    Google Scholar 

  7. Latombe, J.-C.: Robot Motion Planning, 8th edn. Kluwer, Boston, MA (2004)

    Google Scholar 

  8. Choset, H., Lynch, K.M., Hutchison, S., Kantor, G., Burgard, W., Kavarki, L., Thrun, S.: Principles of Robot Motion. Theory, Algorithms and Implementations. MIT Press, Cambridge, MA (2004)

    Google Scholar 

  9. Hamscher, W., Console, L., de Kleer, J. (eds.): Readings in Model-based Diagnosis. Morgan Kaufmann, San Mateo, CA (1992)

    Google Scholar 

  10. Weld, D., de Kleer, J. (eds.): Qualitative Reasoning About Physical Systems. Morgan Kaufmann, San Mateo, CA (1990)

    Google Scholar 

  11. Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  12. de Kleer, J., Williams, B.C.: Diagnosing multiple faults. Artif. Intell. 32(1), 97–130 (1987)

    Article  MATH  Google Scholar 

  13. Kalman, R.: A new approach to linear filtering and prediction problems. ASME Transactions, J. Basic Eng. 82, 35–50 (1960)

    Google Scholar 

  14. Anderson, B., Moore, J.: Optimal Filtering. Information and System Sciences Series. Prentice Hall, Upper Saddle River, NJ (1979)

    Google Scholar 

  15. Isermann, R.: Supervision, fault-detection and fault-diagnosis methods – An introduction. Control Eng. Pract. 5(5), 639–652 (1997)

    Article  Google Scholar 

  16. Chen, J., Patton, R.: Robust Model-based Fault Diagnosis for Dynamic Systems. Kluwer, Boston, MA (1999)

    MATH  Google Scholar 

  17. Hofbaur, M.W., Wotawa, F.: A causal analysis method for concurrent hybrid automata. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06), pp. 840–846 (2006)

  18. Köb, J.: Modellbasierte Regelung eines Roboterfahrwerkes. Master’s thesis, Institute for Automation and Control, Graz University of Technology (2005)

  19. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Man Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  20. Williams, B.C., Nayak, P.: A model-based approach to reactive self-configuring systems. In: Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96), pp. 971–978. American Association for Artificial Intelligence, Menlo Park, CA (1996)

    Google Scholar 

  21. Williams, B.C. et al.: Remote agent: To boldly go where no AI system has gone before. Artif. Intell. 103(1-2), 5–48 (1998)

    Article  MATH  Google Scholar 

  22. Friedrich, G., Stumptner, M., Wotawa, F.: Model-based diagnosis of hardware designs. Artif. Intell. 111(2), 3–39 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  23. Peischl, B., Wotawa, F.: Automated source-level error localization in hardware designs. IEEE Des. Test Comput. 23(1), 8–19 (2006)

    Article  Google Scholar 

  24. Gelb, A.: Applied Optimal Estimation. MIT Press, Cambridge, MA, (1974), 15th printing, 1999 edn.

    Google Scholar 

  25. Ackerson, G.A., Fu, K.S.: On state estimation in switching environments. IEEE Trans. Automat. Contr. 15, 10–17 (1970)

    Article  Google Scholar 

  26. Blom, H.A.P., Bar-Shalom, Y.: The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Automat. Contr. 33, 780–783 (1988)

    Article  MATH  Google Scholar 

  27. Hanlon, P., Maybeck, P.: Multiple-model adaptive estimation using a residual correlation Kalman filter bank. IEEE Trans. Aerosp. Electron. Syst. 36(2), 393–406 (2000)

    Article  Google Scholar 

  28. Narasimhan, S., Biswas, G.: An approach to model-based diagnosis of hybrid systems. In: Tomlin, C.J., Greenstreet, M.R. (eds.) Hybrid Systems: Computation and Control, HSCC volume 2289 of Lecture Notes in Computer Science, pp. 308–322. Springer, Berlin Heidelberg New York (2002)

    Google Scholar 

  29. Zhao, F., Koutsoukos, X., Haussecker, H., Reich, J., Cheung, P.: Distributed monitoring of hybrid systems: a model-directed approach. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’01), pp. 557–564 (2001)

  30. Benazera, E., Travé-Massuyès, L., Dague, P.: State tracking of uncertain hybrid concurrent systems. In: Proceedings of the 13th International Workshop on Principles of Diagnosis (DX02), pp. 106–114, May 2002

  31. Lerner, U., Parr, R., Koller, D., Biswas, G.: Bayesian fault detection and diagnosis in dynamic systems. In: Proceedings of the 7th National Conference on Artificial Intelligence (AAAI’00) (2000)

  32. Dearden, R., Clancy, D.: Particle filters for real-time fault detection in planetary rovers. In: Proceedings of the Thirteenth International Workshop on Principles of Diagnosis, pp. 1–6 (2002)

  33. Verma, V., Gordon, G., Simmons, R., Thrun, S.: Real-time fault diagnosis. IEEE Robot. Autom. Mag. 11(2), 56–66 (2004)

    Article  Google Scholar 

  34. Murphy, R.R., Hershberger, D.: Classifying and recovering from sensing failures in autonomous mobile robots. In: AAAI/IAAI, vol. 2, pp. 922–929. (1996)

  35. Roumeliotis, S.I., Sukhatme, G.S., Bekey, G.A.: Sensor fault detection and identification in a mobile robot. In: IEEE Conf on Intelligent Robots and Systems, pp. 1383–1388, Victoria, Canada (1998)

  36. Steinbauer, G., Wotawa, F.: Detecting and locating faults in the control software of autonomous mobile robots. In: 19th International Joint Conference on Artificial Intelligence (IJCAI-05), Edinburgh, UK (2005)

  37. Steinbauer, G., Mörth, M., Wotawa, F.: Real-time diagnosis and repair of faults of robot control software. In: Proceedings of the RoboCup International Symposium, Osaka, Japan (2005)

  38. Stumptner, M., Wotawa, F.: Model-based reconfiguration. In: Proceedings Artificial Intelligence in Design, Lisbon, Portugal (1998)

  39. Grosclaude, I.: Model-based monitoring of component-based software systems. In: 15th International Workshop on Priciples of Diagnosis, pp. 155–160, Carcassonne, France (2004)

  40. Liu, H., Coghill, G.M.: Qualitative modeling of kinematic robots. In: 18th International Workshop on Qualitative Reasoning, Illinois, USA (2004)

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Authors and Affiliations

  1. Institute for Automation and Control, Graz University of Technology, Inffeldgasse 16c/II, 8010, Graz, Austria

    Michael Hofbaur & Johannes Köb

  2. Institute for Software Technology, Graz University of Technology, Inffeldgasse 16b/II, 8010, Graz, Austria

    Gerald Steinbauer & Franz Wotawa

Authors
  1. Michael Hofbaur
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  2. Johannes Köb
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  3. Gerald Steinbauer
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  4. Franz Wotawa
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Corresponding author

Correspondence to Franz Wotawa.

Additional information

This research has been funded in part by the Austrian Science Fund (FWF) under grant P17963-N04. Authors are listed in alphabetic order.

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Hofbaur, M., Köb, J., Steinbauer, G. et al. Improving Robustness of Mobile Robots Using Model-based Reasoning. J Intell Robot Syst 48, 37–54 (2007). https://doi.org/10.1007/s10846-006-9102-0

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  • DOI: https://doi.org/10.1007/s10846-006-9102-0

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