Journal of Bionic Engineering ›› 2020, Vol. 17 ›› Issue (1): 134-147.doi: 10.1007/s42235-020-0011-x

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Active Balance Control of Humanoid Locomotion Based on Foot Position Compensation

Chengju Liu1, Tong Zhang1, Ming Liu2, Qijun Chen1*   

  1. 1. School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
    2. School of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China
  • 收稿日期:2019-04-12 修回日期:2019-12-12 接受日期:2019-12-16 出版日期:2020-01-10 发布日期:2020-01-21
  • 通讯作者: Qijun Chen, Senior member, IEEE E-mail:qjchen@tongji.edu.cn
  • 作者简介:Chengju Liu1, Tong Zhang1, Ming Liu2, Qijun Chen1*

Active Balance Control of Humanoid Locomotion Based on Foot Position Compensation

Chengju Liu1, Tong Zhang1, Ming Liu2, Qijun Chen1*   

  1. 1. School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
    2. School of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China
  • Received:2019-04-12 Revised:2019-12-12 Accepted:2019-12-16 Online:2020-01-10 Published:2020-01-21
  • Contact: Qijun Chen, Senior member, IEEE E-mail:qjchen@tongji.edu.cn
  • About author:Chengju Liu1, Tong Zhang1, Ming Liu2, Qijun Chen1*

摘要: A foot positioning compensator is developed in this paper for a full-body humanoid to retrieve its balance during continuous walking.
An online Foot Position Compensator (FPC) is designed to improve the robustness of biped walking, which can modify predefined step
position and step duration online with sensory feedback. Foot placement parameters are learned by the FPC based on the Policy Gradient
Reinforcement Learning (PGRL) method. Moreover, the FPC assists the humanoid robot in rejecting external disturbances and recovering
the walking position by re-planning the trajectories of walking pattern and the Center of Mass (CoM). An upper body pose control strategy
is also presented to further enhance the performance of humanoid robots to overcome strong external disturbances. The advantages of this proposed method are that it neither requires prior information about the walking terrain conditions, nor relies on range sensor information for surface topology measurement. The effectiveness of the proposed method is verified via Webots simulation and real experiments on a full-body humanoid NAO robot.

关键词: humanoid walking, active balance, Foot Positioning Compensation (FPC), Policy Gradient Reinforcement Learning (PGRL)

Abstract: A foot positioning compensator is developed in this paper for a full-body humanoid to retrieve its balance during continuous walking.
An online Foot Position Compensator (FPC) is designed to improve the robustness of biped walking, which can modify predefined step
position and step duration online with sensory feedback. Foot placement parameters are learned by the FPC based on the Policy Gradient
Reinforcement Learning (PGRL) method. Moreover, the FPC assists the humanoid robot in rejecting external disturbances and recovering
the walking position by re-planning the trajectories of walking pattern and the Center of Mass (CoM). An upper body pose control strategy
is also presented to further enhance the performance of humanoid robots to overcome strong external disturbances. The advantages of this proposed method are that it neither requires prior information about the walking terrain conditions, nor relies on range sensor information for surface topology measurement. The effectiveness of the proposed method is verified via Webots simulation and real experiments on a full-body humanoid NAO robot.

Key words: humanoid walking, active balance, Foot Positioning Compensation (FPC), Policy Gradient Reinforcement Learning (PGRL)