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Fall detection in walking robots by multi-way principal component analysis

Published online by Cambridge University Press:  01 March 2009

J. G. Daniël Karssen  and
Martijn Wisse
J. G. Daniël Karssen*
Affiliation:
Department of Mechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.
Martijn Wisse
Affiliation:
Department of Mechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.
*
*Corresponding author. E-mail: j.g.d.karssen@tudelft.nl
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Summary

Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in a simulation study with the simplest walking model. The results of this study show that the MPCA method is able to predict a fall up to four steps in advance in the case of single disturbances. In the case of random disturbances the MPCA method has a successful detection probability of up to 90%.

Keywords

BipedsLegged robotsHumanoid robotsRobot dynamicsPose estimation and registration
Type
Article
Information
Robotica , Volume 27 , Issue 2 , March 2009 , pp. 249 - 257
Copyright
Copyright © Cambridge University Press 2008

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