基于动态运动原语和自适应控制的机器人技能学习

doi:10.16183/j.cnki.jsjtu.2021.379

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (3): 354-365.doi: 10.16183/j.cnki.jsjtu.2021.379

所属专题：《上海交通大学学报》2023年“电子信息与电气工程”专题

基于动态运动原语和自适应控制的机器人技能学习

张文安(), 高伟展, 刘安东

浙江工业大学信息工程学院,杭州 310023

收稿日期:2021-09-26 接受日期:2021-11-05 出版日期:2023-03-28 发布日期:2023-03-30
作者简介:张文安(1982-),教授,博士生导师,现主要从事多源信息融合、机器人技能学习研究;E-mail:wazhang@zjut.edu.cn.
基金资助:
浙江省自然科学基金重大项目(LD21F030002)

Robot Skill Learning Based on Dynamic Motion Primitives and Adaptive Control

ZHANG Wenan(), GAO Weizhan, LIU Andong

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Received:2021-09-26 Accepted:2021-11-05 Online:2023-03-28 Published:2023-03-30

摘要/Abstract

摘要：

提出一种基于动态运动原语(DMP)和自适应控制的机器人技能学习方法. 现有的DMP从单示教轨迹中学习动作,且其高斯基函数分布方式固定,并不适用于各种不同特征的动作轨迹. 因此,将高斯混合模型和高斯混合回归引入DMP中,使其能从多示教轨迹中学习技能,并且将径向基神经网络(RBFNN)引入DMP中构成RBF-DMP方法,以梯度下降的方式学习高斯基中心位置和权重,提高技能学习的精度.设计自适应神经网络控制器,用于控制机械臂复现示教中学习的动作. 在Franka Emika Panda协作机械臂上开展实验研究,验证方法的有效性.

关键词: 动态运动原语, 高斯混合模型, 径向基神经网络, 机器人学习

Abstract:

A novel robot skill learning method using dynamic movement primitive (DMP) and adaptive control is proposed. The existing DMP method learns actions from a single teaching trajectory, and its Gaussian basis function distribution mode is fixed, which is not suitable for multiple movement trajectories with different characteristics. Therefore, the Gaussian mixture model (GMM) and Gaussian mixture regression are introduced into DMP to enable the robot to learn skills from multi-teaching trajectory. Moreover, radial basis function neural network (RBFNN) is introduced into DMP to establish the RBF-DMP method, which is able to learn the central position and weight of Gaussian basis through gradient descent and improves the accuracy of skill modeling. Furthermore, an adaptive neural network controller is designed to control the learned actions of the manipulator in redemonstration. Finally, experiments on Franka Emika Panda manipulator prove the effectiveness of the proposed method.

Key words: dynamic movement primitive (DMP), Gaussian mixture model (GMM), radial basis function neural network (RBFNN), robot learning

中图分类号:

TP242

张文安, 高伟展, 刘安东. 基于动态运动原语和自适应控制的机器人技能学习[J]. 上海交通大学学报, 2023, 57(3): 354-365.

ZHANG Wenan, GAO Weizhan, LIU Andong. Robot Skill Learning Based on Dynamic Motion Primitives and Adaptive Control[J]. Journal of Shanghai Jiao Tong University, 2023, 57(3): 354-365.

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方法	总MSE	总建模时间/s
DMP	1.628	12.91
RBF-DMP	1.011	16.24
拓展框架	1.193	13.98

基于动态运动原语和自适应控制的机器人技能学习

Robot Skill Learning Based on Dynamic Motion Primitives and Adaptive Control

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