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Abstract

In this paper, a probability-based balance monitoring concept for humanoid robots is proposed. Two algorithms are presented that allow us to distinguish between exceptional situations and normal operations. The first classification approach uses Gaussian-Mixture-Models (GMM) to describe the distribution of the robot's sensor data for typical situations such as stable walking or falling down. With the GMM it is possible to state the probability of the robot being in one of the known situations. The concept of the second algorithm is based on Hidden-Markov-Models (HMM). The objective is to detect and classify unstable situations by means of their typical sequences in the robot's sensor data. When appropriate reflex motions are linked to the critical situations, the robot can prevent most falls or is at least able to execute a controlled falling motion. The proposed algorithms are verified by simulations and experiments with our bipedal robot BARt-UH.

Keywords

robot balance monitoring fall detection reflex motions HMM viability kernel classification

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References

Albert, A. (2002). Intelligente Bahnplanung und Regelung für einen autonomen, zweibeinigen Roboter. VDI Verlag, Düsseldorf.

Bellman, R.E. (1957). Dynamic Programming. Princeton University Press, Princeton, New Jersey.

Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer, Science + Business Media, LLC, New York.

Bortolami, S. , DiZio, P. , Rabin, E. , and Lackner, J. (2003). Analysis of human postural responses to recoverable falls. Experimental Brain Research, 151: 387-404.

Durbin, R. , Eddy, S. , Krogh, A. , and Mitchinson, G. (1998). Biological sequence analysis - Probabilistic models of proteins and nucleic acids. Cambridge University Press , Cambridge.

Fink, G.A. (2008). Markov Models for Pattern Recognition. Springer, Berlin.

Fujiwara, K. , Kajita, S. , Harada, K. , Kaneko, K. , Morisawa, M. , Kanehiro, F. , and Hirukawa, S.N.H. (2006). Towards an optimal falling motion for a humanoid robot. Humanoids,: 524-529.

Fujiwara, K. , Kanehiro, F. , Kajita, S. , Kaneko, K. , Yokoi, K. , and Hirukawa, H. (2002). UKEMI: Falling motion control to minimize damage to biped humanoid robot. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems EPFL , Lausanne, Switzerland.

Fujiwara, K. , Kanehiro, F. , Kajita, S. , Yokoi, K. , Saito, H. , Harada, K. , Kaneko, K. , and Hirukawa, H. (2003). The first human-size humanoid that can fall over safely and stand-up again. In Proceedings of the 2003 International Conference on Robots and Systems, Las Vegas, Nevada, Volume 2, pp. 27-31.

10.

Fujiwara, K. , Kanehiro, F. , Saito, H. , Kajita, S. , Harada, K. , and Hirukawa, H. (2004). Falling motion control of a humanoid robot trained by virtual supplementary tests. In Proceedings of the 2004 IEEE International Conference on Robotics & Automation, New Orleans, LA, pp. 1077-1082.

11.

Fukunaga, K. (1972). Introduction to Statistical Pattern Recognition . Academic Press, Inc., New York .

12.

Goswami, A. (1999). Foot rotation indicator (FRI) point: A new gait planning tool to evaluate postural stability of biped robots. In Proceedings of the 1999 IEEE International Conference on Robotics & Automation, Detroit, Michigan, pp. 47-52.

13.

Höhn, O. , Gacnik, J. , and Gerth, W. (2005). Detection and classification of posture instabilities of bipedal robots. In Proceedings of the 8th International Conference on Climbing and Walking Robots, London, UK, pp. 409-416. Springer.

14.

Höhn, O. , Schollmeyer, M. , and Gerth, W. (2004). Sturzvermeidung von zweibeinigen Robotern durch reflexartige Reaktionen. In Eingebettete Systeme, Informatik aktuell, pp. 60-69. Springer.

15.

Morisawa, M. , Kajita, S. , Harada, K. , Kaneko, K. , Kanehiro, F. , Fujiwara, K. , and Hirukawa, H. (2005). Emergency stop algorithm for walking humanoid robots. In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2109-2115.

16.

Popovic, M.B. , Goswami, A. , and Herr, H. (2005). Ground reference points in legged locomotion: Definitions, biological trajectories and control implications. The International Journal of Robotics Research, 24(12): 1013-1032.

17.

Rabiner, L. and Juang, B.-H. (1993). Fundamentals of Speech Recognition. Prentice Hall, Inc, Englewood Cliffs, New Jersey.

18.

Rabiner, L.R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE , 77(2): 257-285.

19.

Robonovitch, S.N. , Hayes, W.C. , and McMahon, T.A. (1995). Energy-shunting hip padding system attenuates femoral impact force in a simulated fall. Journal of Biomechanical Engineering, 117: 409-413.

20.

Strasser, R. , Seebode, M. , and Gerth, W. (2003). A very small low cost Inertial Measurement Unit (IMU) for robotic applications. Symposium Gyro Technology ,: 18.0-18.9.

21.

Vukobratović, M. and Borovac, B. (2004). Zero-Moment Point - Thirty Five Years of its Life . International Journal of Humanoid Robotics, 1(1): 157-173.

22.

Wieber, P.B. (2002). On the stability of walking systems. In Proceedings of the International Workshop on Humanoid and Human Friendly Robotics, pp. 53-59, Tsukuba, Japan.

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