Robust Dynamic Walking for Humanoid Robots via Computationally Efficient Footstep Planner and Whole-Body Control

Abstract

The robust dynamic walking control of humanoid robots is the foundation for their application in complex scenarios. Model Predictive Control (MPC) can predict the robot’s motion state over a future time horizon to obtain optimal control inputs and has achieved significant success in walking control of legged robots. However, the high computational cost makes it challenging to ensure real-time performance when dealing with complex dynamic systems. A novel dynamic walking control framework for humanoid robots is introduced in this paper by integrating an efficient footstep planner and Task Space Inverse Dynamics based Whole Body Controller (TSID-WBC). This framework aims to enhance the dynamic performance and robustness of the humanoid robot’s walking control. Firstly, design a bionic step time generator based on relevant research in biology. Then, the MPC controller, combined with the differential equation solution of the Linear Inverted Pendulum (LIP) dynamics model, is used to predict the optimal footsteps for the humanoid robot. Finally, TSID-WBC is employed to optimize joint torques, joint accelerations, and generalized contact forces based on predefined task priorities. The proposed framework avoids using MPC for predictive control of complex dynamic models. Instead, it innovatively integrates the bionic step time generator proposed in this paper with an MPC based on the differential solution of the LIP dynamics model, resulting in an efficient footstep planner. The simulation and experimental results of the humanoid robot Kuavo demonstrate that the proposed control framework achieves better tracking performance for desired walking speeds and responds quickly to external disturbances.