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一种基于DTW-GMM的机器人多机械臂多任务协同策略

刘成菊 林立民 刘明 陈启军

刘成菊, 林立民, 刘明, 陈启军. 一种基于DTW-GMM的机器人多机械臂多任务协同策略. 自动化学报, 2022, 48(9): 2187−2197 doi: 10.16383/j.aas.c190817
引用本文: 刘成菊, 林立民, 刘明, 陈启军. 一种基于DTW-GMM的机器人多机械臂多任务协同策略. 自动化学报, 2022, 48(9): 2187−2197 doi: 10.16383/j.aas.c190817
Liu Cheng-Ju, Lin Li-Min, Liu Ming, Chen Qi-Jun. A multi-task collaborative strategy for multi-arm robot based on DTW-GMM. Acta Automatica Sinica, 2022, 48(9): 2187−2197 doi: 10.16383/j.aas.c190817
Citation: Liu Cheng-Ju, Lin Li-Min, Liu Ming, Chen Qi-Jun. A multi-task collaborative strategy for multi-arm robot based on DTW-GMM. Acta Automatica Sinica, 2022, 48(9): 2187−2197 doi: 10.16383/j.aas.c190817

一种基于DTW-GMM的机器人多机械臂多任务协同策略

doi: 10.16383/j.aas.c190817
基金项目: 国家自然科学基金 (61733013, 62173248, 61673300)和苏州市重点产业技术创新关键核心技术研发项目(SGC2021035)资助
详细信息
    作者简介:

    刘成菊:同济大学电子与信息工程学院教授. 2011年获同济大学博士学位. 主要研究方向为智能控制, 机器人运动控制和进化计算E-mail: liuchengju@tongji.edu.cn

    林立民:同济大学电子与信息工程学院硕士研究生. 主要研究方向为机器人运动控制.E-mail: linlimin0722@126.com

    刘明:中国香港科技大学电子与计算机工程学系助理教授. 2013年获苏黎世联邦理工学院博士学位. 主要研究方向为机器人运动控制, 环境建模和感知. E-mail: eelium@ust.hk

    陈启军:同济大学电子与信息工程学院教授. 1999年获同济大学博士学位. 主要研究方向为机器人与人工智能. 本文通信作者.E-mail: qjchen@tongji.edu.cn

A Multi-task Collaborative Strategy for Multi-arm Robot Based on DTW-GMM

Funds: Supported by National Natural Science Foundation of China (61733013, 62173248, 61673300) and Suzhou Key Industry Technological Innovation-core Technology R&D Program (SGC-2021035)
More Information
    Author Bio:

    LIU Cheng-Ju  Professor at the College of Electrical and Information Engineering, Tongji University. She received her Ph.D. degree from Tongji University in 2011. Her research interest covers intelligent control, motion control of robots and evolutionary computation

    LIN Li-Min  Master student at the College of Electronics and Information Engineering, Tongji University. His main research interest is robot motion control

    LIU Ming  Assistant professor in the Department of Electronics and Computer Engineering, Hong Kong University of Science and Technology, China. He received his Ph.D. degree from Swiss Federal Institute of Technology Zur-ich. His research interest covers robot motion control, environmental modeling and perception

    CHEN Qi-Jun  Professor at the College of Electronic and Information Engineering, Tongji University. He received his Ph.D. degree from Tongji University in 1999. His research interest covers robotics and artificial intelligence. Corresponding author of this paper

  • 摘要: 为了控制机器人完成复杂的多臂协作任务, 提出了一种基于动态时间规整−高斯混合模型(Dynamic time warping-Gaussian mixture model, DTW-GMM)的机器人多机械臂多任务协同策略. 首先, 针对机器人示教时轨迹时间长短往往存在较大差异的问题, 采用动态时间规整方法来统一时间的变化; 其次, 基于动态时间规整的多机械臂示教轨迹, 采用高斯混合模型对轨迹的特征进行提取, 并以某一机械臂的位置空间矢量作为查询向量, 基于高斯混合回归泛化输出其余机械臂的执行轨迹; 最后, 在Pepper仿人机器人平台上验证了所提出的多机械臂协同策略, 基于DTW-GMM算法控制机器人完成了双臂协作搬运任务和汉字轨迹的书写任务. 提出的基于DTW-GMM算法的多任务协同策略简单有效, 可以利用反馈信息实时协调各机械臂的任务, 在线生成平滑的协同轨迹, 控制机器人完成复杂的协作操作.
  • 图  1  动态时间规整算法

    Fig.  1  DTW algorithm

    图  2  双臂协同轨迹泛化输出

    Fig.  2  Generalized dual-arm collaborative trajectory output

    图  3  抗干扰性输出

    Fig.  3  Anti-disturbance output

    图  4  总体系统架构设计

    Fig.  4  The system architecture block diagram

    图  5  多机械臂协同轨迹生成器框图

    Fig.  5  Multi-arm collaborative trajectory generator block diagram

    图  6  基于微分逆运动学的机器人运动控制引擎设计框图

    Fig.  6  Block diagram of the motion control engine based on differential inverse kinematics

    图  7  Pepper实验平台

    Fig.  7  Pepper experimental platform

    图  8  双臂协作搬运示教

    Fig.  8  Demonstrations of dual-arm collaborative moving trajectory

    图  9  右手臂轨迹DTW规整

    Fig.  9  DTW output of right arm trajectory

    图  10  右手臂轨迹表征

    Fig.  10  Right arm trajectory characterization

    图  11  右手臂轨迹泛化输出

    Fig.  11  Generalized right arm trajectory output

    图  12  搬运实验左右手臂轨迹泛化输出

    Fig.  12  Generalized dual-arm trajectory output in moving experiment

    图  13  双臂协作搬运实验截图

    Fig.  13  Snapshots of dual-arm to collaboratively move basket

    图  14  基于DTW-GMM算法的汉字轨迹书写

    Fig.  14  Chinese character trajectory generation based on DTW-GMM algorithm

    表  1  算法的时间复杂度

    Table  1  Time complexity of algorithms

    轨迹规划算法 时间复杂度
    DP ${n_x} {n_y}$
    GCTW $2dl{{m} } + 8{{ {m^3} } }$
    下载: 导出CSV

    表  2  DTW-GMM算法参数设置

    Table  2  Parameter setting of the DTW-GMM algorithm

    参数 参数值
    $\delta$ $1 \times {10^{{\rm{ - 10}}}}$
    ${i_{\max }}$ 10000
    $\lambda$ 0.0001
    下载: 导出CSV

    表  3  GMM表征协方差矩阵表

    Table  3  Covariance matrix of GMM algorithm

    变量 t x y z
    t 1 0.0814 −0.0074 0.0280
    x 0.0814 1 −0.0003 0.0010
    y −0.0074 −0.0003 1 −0.0001
    z 0.0280 0.0010 −0.0001 1
    下载: 导出CSV

    表  4  DTW-GMM表征协方差矩阵表

    Table  4  Covariance matrix of DTW-GMM algorithm

    变量 t x y z
    t 1 0.0078 −0.0063 0.0122
    x 0.0078 1 −0.0001 0.0002
    y −0.0063 −0.0001 1 0.0001
    z 0.0122 0.0010 0.0001 1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-02
  • 录用日期:  2020-02-23
  • 网络出版日期:  2022-09-05
  • 刊出日期:  2022-09-16

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