Falling avoidance of biped robot using state classification

Abstract

This paper introduce a state classification method for detecting falling of biped robot. The method uses a support vector machine (SVM) to classify the state. The input vector for the SVM are a magnitude of acceleration, a position of center of pressure (CoP) in x and z axis, and tilt angles of torso relative to x and z axis. The input vector is based on sensor data that is measured from accelerometer and force sensing resistor (FSR) sensor. Training of the classifier is done in off-line and the trained classifier is used to classify the state of the biped robot in on-line. The method was verified in a 3D dynamics simulator and showed it could classify falling state within 0.01 second.