基于节律动态运动基元与机器人动力学的多空间融合周期性变阻抗技能学习  

Multi-space Fused Periodic Variable Impedance Skill Learning Based on Rhythmic Dynamic Movement Primitive and Robot Dynamics

作　　者：刘程果 合烨[1,2] 陈小安[1,2] 王光建[1,2] LIU Chengguo;HE Ye;CHEN Xiaoan;WANG Guangjian(College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044;State Key Laboratory of Mechanical Transmission for Advanced Equipment,Chongqing University,Chongqing 400044)

机构地区：[1]重庆大学机械与运载工程学院,重庆400044 [2]重庆大学高端装备机械传动全国重点实验室,重庆400044

出　　处：《机械工程学报》2025年第1期150-161,共12页Journal of Mechanical Engineering

基　　金：国家重点研发计划(2017YFB1301401);国家自然科学基金(92048201)资助项目。

摘　　要：当前人-机器人技能传递方法主要集中于离散的点到点或运动学方面,而周期性的动力学技能学习没有被恰当考虑。本文提出了一种基于节律动态运动基元的多空间融合技能学习框架用于从人类示范中学习周期性变阻抗技能(包括运动轨迹与变刚度特征)。机器人工作空间的力感知信息被用于估计端点刚度矩阵,但由于其对称正定(Symmetric positive definite,SPD)特性与传统节律动态运动基元(Rhythmic dynamic movement primitive,rDMP)对欧氏空间数据的依赖性,研究设计了一种基于黎曼度量的节律动态运动基元(Riemannian metric-based rhythmic dynamic movement primitive,RM-rDMP)技能学习方法以适应SPD流形上的刚度矩阵信息,并采用自适应振荡器估计系统的频率与相位。通过基于机器人动力学模型的阻抗控制策略,将运动轨迹与变刚度特性同时赋予机器人以完成变阻抗技能编码。通过仿真与实验研究表明,所提方法可成功将类人的运动学与动力学技能传递给机器人,并使其具备再现与泛化能力。综上,所提方法包括一个端点刚度估计模型和一个多空间融合的周期性变阻抗技能学习框架,适用于需要同时考虑欧氏空间位置和黎曼空间刚度矩阵的人-机器人技能传递控制方法。The current methods for human-robot skill transfer in robotics mainly focus on discrete point-to-point or kinematic aspects,while the learning of periodic dynamic skills has not been adequately considered.This article proposes a multi-space fusion skill learning framework based on rhythmic dynamic movement primitive(rDMP)for learning periodic impedance skills(including motion trajectories and variable stiffness features)from human demonstrations.The force perception information of the robot’s workspace is used to estimate the endpoint stiffness matrix.However,due to its symmetric positive definite(SPD)characteristics and the dependence of traditional rDMP on Euclidean space data,this research designs a Riemannian metric-based rhythmic dynamic movement primitive(RM-rDMP)skill learning method to adapt to the stiffness matrix information on the SPD manifold and adopts an adaptive oscillator estimation system for frequency and phase.By using an impedance control strategy based on the robot’s dynamics model,both motion trajectories and variable stiffness features are assigned to the robot to achieve variable impedance skill encoding.Simulation and experimental studies demonstrate that the proposed method successfully transfers human-like kinematic and dynamic skills to the robot and enables it to have reproduction and generalization capabilities.In summary,the proposed method includes an endpoint stiffness estimation model and a multi-space fusion framework for periodic impedance skill learning,which is applicable to human-robot skill transfer control methods that need to consider both Euclidean space position and Riemannian space stiffness matrix.

关 键 词：机器人动力学 周期性可变阻抗技能 节律动态运动基元 黎曼流形 人-机器人技能传递 

分 类 号：TH113[机械工程—机械设计及理论]