A novel gait and slip detection algorithm for walking robots using an inertial measurement unit was developed. An unscented Kalman filter was formulated with a simple dynamic model as a block on a slope without translations. Considerable prediction errors resulted when unmodeled dynamics (i.e., translation) occurred. These prediction errors were used in a binary Bayes filter to estimate the probability of gait and slip states. A proof of concept experiment was conducted with a monopedal walker under three floor conditions (nonslip, poly, and poly-oil) and three orientations (flat, uphill, and downhill). Realtime and offline detection at 100 Hz were successful. Continuous gait cycles were detected in proper order. Slip detection was successful except for very mild slips involving small jerk. The proposed algorithm provided a robust gait and slip detection method with a single set of parameters without knowledge of floor conditions and inclinations.

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