基于运动发散分量的仿人机器人稳健行走

Robust Walking for Humanoid Robot Based on Divergent Component of Motion.

作者信息

Zhang Zhao, Zhang Lei, Xin Shan, Xiao Ning, Wen Xiaoyan

机构信息

College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102612, China.

出版信息

Micromachines (Basel). 2022 Jul 11;13(7):1095. doi: 10.3390/mi13071095.

DOI:10.3390/mi13071095

PMID:35888912

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9324268/

Abstract

In order to perform various complex tasks in place of humans, humanoid robots should walk robustly in the presence of interference. In the paper, an improved model predictive control (MPC) method based on the divergent components of motion (DCM) is proposed. Firstly, the humanoid robot model is simplified to a finite-sized foot-pendulum model. Then, the gait of the humanoid robot in the single-support phase (SSP) and double-support phase (DSP) is planned based on DCM. The center of mass (CoM) of the robot will converge to the DCM, which simplifies the feedback control process. Finally, an MPC controller incorporating an extended Kalman filter (EKF) is proposed to realize the tracking of the desired DCM trajectory. By adjusting the step duration, the controller can compensate for CoM trajectory errors caused by disturbances. Simulation results show that-compared with the traditional method-the method we propose achieves improvements in both disturbed walking and uneven-terrain walking.

摘要

为了代替人类执行各种复杂任务，类人机器人应在存在干扰的情况下稳健行走。本文提出了一种基于运动发散分量（DCM）的改进模型预测控制（MPC）方法。首先，将类人机器人模型简化为有限尺寸的足摆模型。然后，基于DCM规划类人机器人在单支撑阶段（SSP）和双支撑阶段（DSP）的步态。机器人的质心（CoM）将收敛到DCM，这简化了反馈控制过程。最后，提出了一种结合扩展卡尔曼滤波器（EKF）的MPC控制器，以实现对期望DCM轨迹的跟踪。通过调整步长时间，控制器可以补偿由干扰引起的CoM轨迹误差。仿真结果表明，与传统方法相比，我们提出的方法在干扰行走和不平坦地形行走方面均有改进。