Online Gait Generation For NAO Humanoid Robot Using Model Predictive Control

Abstract

This manuscript presents an effective method for online real time walking pattern generation using non-linear optimization of the model predictive control cost function under given constraints and applies this method to the NAO humanoid robot to achieve stable dynamic walking. This paper explains how the data generated is used to create a balanced robot center of mass trajectory along with smooth feet trajectories. The paper introduces a computationally efficient method for joint angle trajectory generation using inverse kinematics combined with iterative optimization to achieve desired center of mass trajectory. Finally the above mentioned modules were integrated together in MATLAB/SIMULINK environment to construct a NAO gait generation engine based on desired robot walking path input and the overall ZMP root mean square error in steady state is around 0.002 m which is very small relative to the support polygon area.