A Reactive Planning and Control Framework for Humanoid Robot Locomotion

Abstract

This article presents a reactive planning and control framework to enhance the robustness of humanoid robots locomotion against external disturbances. The framework comprises two main modules, reactive planning and motion optimization. In the reactive planning module, a reactive footstep compensation strategy based on the essential motion of the linear inverted pendulum model (LIPM) is proposed. This strategy leverages the periodic motion characteristics of the LIPM, deriving the correct footstep compensation based on the conditions for model stability restoration. The module generates the zero moment point planning trajectories based on the footstep compensation. In the motion optimization module, motion optimization based on reactive planning is performed. To make motion constraint based on capture point applicable to motion optimization, the impact of different truncation points on stability constraints to determine the appropriate truncation point is quantified. The effectiveness of the proposed framework is demonstrated through experiments conducted on the humanoid robot UBTECH Walker2.