Humanoid robot push-recovery strategy based on CMP criterion and angular momentum regulation

Abstract

We propose a push-recovery strategy to stabilize the robot under unmodelled, large external forces. The strategy integrates Center-Of-Gravity (COG) angular momentum regulator, COG state estimator, and stepping control, which online modifies the trajectories of the COG and the swing leg. Using the centroidal-moment-pivot criterion, the COG angular momentum regulator controls the dynamics of the COG as an impedance system through the feedback of COG state estimator based on Kalman filter. The stepping control, on the other hand, selects the appropriate balancing reaction in anticipation of the potential consequences of the external disturbances on the robot. In simulations and experiments, we show the proposed push-recovery strategy can effectively save the robot from falling down and walk more smoothly.