Foot Stepping Algorithm of Humanoids with Double Support Time Adjustment based on Capture Point Control

Recently, foot stepping strategies of humanoid robots have been actively developed for robust balancing of humanoids against disturbances. In this paper, a novel stepping algorithm adjusting double support phase (DSP) time is proposed. First, the stepping algorithm is proposed based on a model predictive control (MPC) framework for capture point (CP) control and footstep adjustment. Next, when the remaining step time is not enough to adjust the footstep, the DSP scaling method brings the next swing phase forward by reducing the DSP time, which enables the robot to maintain the balance robustly. The robust balance control performance of the proposed method is validated through simulations and experiments when the robot is walking in the presence of external pushes. A more stable balancing performance is realized compared to state-of-the-art stepping controllers.