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The International Journal of Robotics Research
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Probabilistic Balance Monitoring for Bipedal Robots

O. Höhn

Baker Hughes, INTEQ GmbH, 29221 Celle, Germany, oliver.hoehn{at}bakerhughes.com

W. Gerth

Institute of Automatic Control Leibniz University of Hannover 30167 Hannover, hoehn{at}irt.uni-hannover.de

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.

Key Words: robot • balance monitoring • fall detection • reflex motions • HMM • viability kernel • classification

The International Journal of Robotics Research, Vol. 28, No. 2, 245-256 (2009)
DOI: 10.1177/0278364908095170
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