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

作　　者：张文安[1] 高伟展 刘安东[1] ZHANG Wenan;GAO Weizhan;LIU Andong(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]浙江工业大学信息工程学院,杭州310023

出　　处：《上海交通大学学报》2023年第3期354-365,共12页Journal of Shanghai Jiaotong University

基　　金：浙江省自然科学基金重大项目(LD21F030002)。

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.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.

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 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]