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基于自学习中枢模式发生器的仿人机器人适应性行走控制

刘成菊 耿烷东 张长柱 陈启军

刘成菊, 耿烷东, 张长柱, 陈启军. 基于自学习中枢模式发生器的仿人机器人适应性行走控制. 自动化学报, 2021, 47(9): 2170−2181 doi: 10.16383/j.aas.c190087
引用本文: 刘成菊, 耿烷东, 张长柱, 陈启军. 基于自学习中枢模式发生器的仿人机器人适应性行走控制. 自动化学报, 2021, 47(9): 2170−2181 doi: 10.16383/j.aas.c190087
Liu Cheng-Ju, Geng Wan-Dong, Zhang Chang-Zhu, Chen Qi-Jun. Adaptive locomotion control of humanoid robot based on self-learning CPG. Acta Automatica Sinica, 2021, 47(9): 2170−2181 doi: 10.16383/j.aas.c190087
Citation: Liu Cheng-Ju, Geng Wan-Dong, Zhang Chang-Zhu, Chen Qi-Jun. Adaptive locomotion control of humanoid robot based on self-learning CPG. Acta Automatica Sinica, 2021, 47(9): 2170−2181 doi: 10.16383/j.aas.c190087

基于自学习中枢模式发生器的仿人机器人适应性行走控制

doi: 10.16383/j.aas.c190087
基金项目: 

国家自然科学基金 62173248

国家自然科学基金 U1713211

国家自然科学基金 61673300

浦东新区科技发展基金 PKX2019-R18

详细信息
    作者简介:

    耿烷东  同济大学电子与信息工程学院硕士研究生.主要研究方向为仿生技术, 机器人运动控制. E-mail: gengwandong@foxmail.com

    张长柱  同济大学电子与信息工程学院副教授.主要研究方向为智能控制, 网络控制系统, 信号处理和自动驾驶. E-mail: zhang_changzhu@tongji.edu.cn

    陈启军 同济大学电子与信息工程学院教授.主要研究方向为机器人运动控制, 智能控制, 嵌入式系统. E-mail: qjchen@tongji.edu.cn

    通讯作者:

    LIU Cheng-Ju   Professor at the College of Electronic and Information Engineering, Tongji University. Her research interest covers bionics, robot motion control, and evolutionary computation. Corresponding author of this paper

  • 本文责任编委 刘艳军

Adaptive Locomotion Control of Humanoid Robot Based on Self-Learning CPG

Funds: 

National Natural Science Foundation of China 62173248

National Natural Science Foundation of China U1713211

National Natural Science Foundation of China 61673300

Pudong New Area Science and Technology Development Fund PKX2019-R18

More Information
    Author Bio:

    GENG Wan-Dong   Master student at the College of Electronic and Information Engineering, Tongji University. His research interest covers bionics and robot motion control

    ZHANG Chang-Zhu   Associate professor at the College of Electronic and Information Engineering, Tongji University. His research interest covers intelligent control, networked control systems, signal processing, and autonomous driving

    CHEN Qi-Jun  Professor at the College of Electronic and Information Engineering, Tongji University. His research interest covers robot motion control, intelligent control, and embedded systems

  • Recommended by Associate Editor LIU Yan-Jun
  • 摘要: 为了克服传统中枢模式发生器(Central pattern generator, CPG)关节空间控制方法的复杂性和局限性, 本文基于自学习中枢模式发生器模型, 提出了一套在线调制和融合多传感器信息的仿人机器人环境自适应行走控制方法.算法难点在于如何在机器人的工作空间将自学习CPG用于工作空间轨迹生成, 并使CPG参数直接和步态模式相关联.本文提出了利用自学习CPG来学习和实时生成机器人质心轨迹和脚掌轨迹的方法, 在线调节机器人步长、抬腿高度和步行速度等关键参数.参考生物反射行为, 利用传感反馈信息激发CPG以产生具有环境适应性的工作空间轨迹, 提升行走质量. 控制系统的参数通过优化算法来进一步改善行走性能.相比于传统的CPG关节空间法, 本文所采用的自学习CPG工作空间法不仅极大简化了CPG网络结构而且提高了仿人机器人行走的适应性.最后, 通过仿人机器人坡面适应性行走的仿真和实验, 验证了所提出控制策略的可行性和有效性.
    Recommended by Associate Editor LIU Yan-Jun
    1)  本文责任编委 刘艳军
  • 图  1  自学习CPG模型学习基本过程

    Fig.  1  Basic learning process of SL-CPG model

    图  2  自学习CPG学习结果

    Fig.  2  The learning results of SL-CPG

    图  3  自学习CPG参数调制

    Fig.  3  The parameters modulation of SL-CPG

    图  4  整体控制系统构架

    Fig.  4  The architecture of the control system

    图  5  质心$x$方向和$y$方向轨迹生成

    Fig.  5  The generated CoM$x$ and CoM$y$ trajectories

    图  6  CPG模型改进前脚掌$x$方向和$z$方向轨迹学习结果

    Fig.  6  The learning results of the $x$ and $z$ direction foot trajectories before CPG model improved

    图  7  CPG模型改进后脚掌$x$方向和$z$方向轨迹学习结果

    Fig.  7  The learning results of the $x$ and $z$ direction foot trajectories after CPG model improved

    图  8  反馈控制回路框图

    Fig.  8  The block diagram of feedback control loop

    图  9  基于微分逆运动学的机器人运动控制引擎设计框图

    Fig.  9  Block diagram of robot motion control engine based on differential inverse kinematics

    图  10  身体姿态角计算

    Fig.  10  Calculation of body attitude

    图  11  迭代180次Pareto前沿面

    Fig.  11  Pareto front of generation 180

    图  12  身体姿态角变化

    Fig.  12  The body attitude angle

    图  13  自学习CPG在线生成的质心CoM$x$和脚掌轨迹

    Fig.  13  Online generated CoM$x$ and foot trajectory by SL-CPG

    图  14  ZMP分布

    Fig.  14  ZMP distribution

    图  15  坡面行走仿真截图

    Fig.  15  Snapshots of d slope terrain adaptive walking simulation experiment

    图  16  身体姿态角变化

    Fig.  16  The body attitude angle

    图  17  变坡度坡面仿真实验截图

    Fig.  17  Snapshots of walking on varying slope

    图  18  CPG网络结构图

    Fig.  18  Network structure of CPG

    图  19  左腿关节控制信号

    Fig.  19  Control signals of left leg joints

    图  20  适应性行走实验截图

    Fig.  20  Snapshots of adaptive walking experiment

    图  21  自学习CPG在线生成的质心CoM$x$和脚掌轨迹

    Fig.  21  Online generated CoM$x$ and foot trajectory by SL-CPG

    图  22  ZMP分布

    Fig.  22  ZMP distribution

    表  1  最优参数集

    Table  1  Optimal parameters set

    参数
    $ K_a $ 0.0137
    $ K_{{\rm CoM}_x} $ 0.0384
    $ K_{{\rm Foot}x} $ 0.0365
    $ K_{{\rm Foot}z} $ 0.0401
    $ K_{\omega} $ 0.0212
    $ K_{\rm sw} $ 0.4813
    $ K_{\rm sup} $ 0.4677
    下载: 导出CSV
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  • 收稿日期:  2019-02-14
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