Biped Walking Trajectory Generator Based on Three-Mass With Angular Momentum Model Using Model Predictive Control

Abstract

In this paper, we present a biped walking trajectory generator based on the three-mass with angular momentum model using model predictive control. This approach aims to decrease the modeling error and decrease zero moment point (ZMP) horizon, so that high ZMP tracking accuracy and immediate generation are achieved. The contribution of this approach is the use of the three-mass with angular momentum model, the reduction of modeling error, and the enhancement of ZMP tracking performance and walking stability. This method allows online walking pattern modification, so that unexpected emergency can be dealt with by immediately changing trajectories. In addition, this proposed method is validated through numerical simulations, and the proof-of-concept experiments are conducted using an experimental robot developed in our laboratory.