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基于改进动态系统稳定估计器的机器人技能学习方法

金聪聪 刘安东 LIUSteven 张文安

金聪聪, 刘安东, LIU Steven, 张文安. 基于改进动态系统稳定估计器的机器人技能学习方法. 自动化学报, 2022, 48(7): 1771−1781 doi: 10.16383/j.aas.c200341
引用本文: 金聪聪, 刘安东, LIU Steven, 张文安. 基于改进动态系统稳定估计器的机器人技能学习方法. 自动化学报, 2022, 48(7): 1771−1781 doi: 10.16383/j.aas.c200341
Jin Cong-Cong, Liu An-Dong, Liu Steven, Zhang Wen-An. A robot skill learning method based on improved stable estimator of dynamical systems. Acta Automatica Sinica, 2022, 48(7): 1771−1781 doi: 10.16383/j.aas.c200341
Citation: Jin Cong-Cong, Liu An-Dong, Liu Steven, Zhang Wen-An. A robot skill learning method based on improved stable estimator of dynamical systems. Acta Automatica Sinica, 2022, 48(7): 1771−1781 doi: 10.16383/j.aas.c200341

基于改进动态系统稳定估计器的机器人技能学习方法

doi: 10.16383/j.aas.c200341
基金项目: 浙江省自然科学基金重大项目(LD21F030002), 国家自然科学基金(61822311, 61973275)资助
详细信息
    作者简介:

    金聪聪:浙江工业大学信息工程学院硕士生. 主要研究方向为人体动作识别和机器人技能学习. E-mail: jcc19960602@gmail.com

    刘安东:浙江工业大学信息工程学院副教授. 主要研究方向为模型预测控制, 网络化控制系统和移动机器人. E-mail: lad@zjut.edu.cn

    LIUSteven:凯泽斯劳滕工业大学电气与计算机工程系教授. 主要研究方向为机电和电力系统控制, 机器人技术, 网络控制和基于模型的故障诊断. E-mail: sliu@eit.uni-kl.de

    张文安:浙江工业大学信息工程学院教授. 主要研究方向为多源信息融合和机器人技能学习. 本文通信作者. E-mail: wazhang@zjut.edu.cn

A Robot Skill Learning Method Based on Improved Stable Estimator of Dynamical Systems

Funds: Supported by Zhejiang Provincial Natural Science Foundation of China (LD21F030002) and National Natural Science Foundation of China (61822311, 61973275)
More Information
    Author Bio:

    JIN Cong-Cong Master student at the College of Information Engineering, Zhejiang University of Technology. His research interest covers human motion recognition and robot skills learning

    LIU An-Dong Associate professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers model predictive control, networked control systems and mobile robots

    LIU Steven Professor in the Department of Electrical and Computer Engineering, University of Kaiserslautern. His research interest covers control of mechatronic and power systems, robotics, networked control and model-based fault diagnosis

    ZHANG Wen-An Professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers information fusion estimation and robot skill learning. Corresponding author of this paper

  • 摘要: 提出一种基于改进动态系统稳定估计器的机器人技能学习方法. 现有的动态系统稳定估计器方法可以通过非线性优化来确保学习系统的全局稳定性, 但是存在确定高斯混合分量个数困难以及稳定性和精度无法兼顾的问题. 因此, 根据贝叶斯非参数模型可以自动确定合适分量个数的特性, 采用狄利克雷过程高斯混合模型对演示进行初始拟合. 随后利用参数化二次李雅普诺夫函数重新推导新的稳定性约束, 有效地解决了动态系统稳定估计器方法中稳定性和精度难以兼顾的问题. 最后, 在LASA数据库和Franka-panda机器人上的实验验证了新方法的有效性和优越性.
  • 图  1  基于SEDS的机器人示教学习流程图

    Fig.  1  Flow chart of LfD based on SEDS

    图  2  DPGMM概率结构图

    Fig.  2  DPGMM probability structure diagram

    图  3  DPGMM对数似然值关于超参数的变化趋势

    Fig.  3  The change trend of DPGMM log-likelihood value on hyperparameters

    图  4  i-SEDS方法在LASA数据集上的复现效果

    Fig.  4  The reproductions of i-SEDS method on LASA dataset

    图  5  SEDS和i-SEDS在不收缩轨迹上的复现结果

    Fig.  5  i-SEDS and SEDS reproductions on the non-contractive demonstrations

    图  6  扫描误差区域(SEA)示意图

    Fig.  6  Schematic diagram of Swept error area (SEA)

    图  7  4种方法在收缩和不收缩轨迹上的SEA分析

    Fig.  7  Performance analysis of four methods on contraction and non-contraction demonstrations with SEA

    图  8  物品搬运任务示教流程图

    Fig.  8  The process of object transport task teaching

    图  9  预处理后的物品搬运任务的演示轨迹

    Fig.  9  Preprocessed demonstrations of object transport task

    图  10  物品搬运任务复现位置和速度轨迹

    Fig.  10  Position and velocity trajectories of reproduction in object transport task

    图  11  机械臂运行时改变目标位置后的轨迹变化

    Fig.  11  Trajectory changes after moving the target container when the robot arm is running

    图  12  对机械臂施加外界扰动时的轨迹变化

    Fig.  12  Trajectory changes when external disturbance is applied to the robot arm

    表  1  4种GMR算法在数据库LASA上的性能比较

    Table  1  Performance comparison of four GMR algorithmson database LASA

    方法总RMSE总训练时间 (${\rm s}$)
    BIC-GMM (EM)269.4375.26
    DdGMM (VI)206.1549.39
    DPGMM (Gibbs)118.58157.48
    DPGMM (VI)130.7939.79
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-22
  • 录用日期:  2020-09-30
  • 网络出版日期:  2022-06-08
  • 刊出日期:  2022-07-01

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