Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.

Augmented Virtual Stiffness Rendering of a Cable-driven SEA for Human-Robot Interaction

Human-robot interaction(HRI) is fundamental for human-centered robotics, and has been attracting intensive research for more than a decade. The series elastic actuator(SEA) provides inherent compliance, safety and further benefits for HRI, but the introduced elastic element also brings control difficulties. In this paper, we address the stiffness rendering problem for a cable-driven SEA system, to achieve either low stiffness for good transparency or high stiffness bigger than the physical spring constant, and to assess the rendering accuracy with quantified metrics. By taking a velocity-sourced model of the motor, a cascaded velocity-torque-impedance control structure is established. To achieve high fidelity torque control, the 2-DOF(degree of freedom) stabilizing control method together with a compensator has been used to handle the competing requirements on tracking performance, noise and disturbance rejection,and energy optimization in the cable-driven SEA system. The conventional passivity requirement for HRI usually leads to a conservative design of the impedance controller, and the rendered stiffness cannot go higher than the physical spring constant. By adding a phase-lead compensator into the impedance controller,the stiffness rendering capability was augmented with guaranteed relaxed passivity. Extensive simulations and experiments have been performed, and the virtual stiffness has been rendered in the extended range of 0.1 to 2.0 times of the physical spring constant with guaranteed relaxed passivity for physical humanrobot interaction below 5 Hz. Quantified metrics also verified good rendering accuracy.

物理性人-机器人交互研究与发展现状

介绍了物理性人-机器人交互的定义,对该过程可能对人造成的伤害进行了分析,给出了物理性人-机器人交互安全评价指标,并总结了目前该领域采用的安全策略。综述了国内外在物理性人-机器人交互方面的研究进展,在分析物理性人-机器人交互研究现状的基础上,阐述了该领域面临的问题和挑战。文章指出,目前物理性人-机器人交互伤害分析和安全评估研究主要集中在钝/锐碰撞造成的骨骼和软组织伤害上,缺乏对脏器、血管、神经等损伤的研究。安全策略主要方法有碰撞避免、包裹软弹性材料、关节柔顺设计等。通过对比各种安全策略的设计思想和组成方案,总结出了各种策略的优缺点和存在的主要问题。由于现有的研究表明单一的手段和策略无法保证人-机器人交互的安全性,提出将认知性人-机器人交互和物理性人-机器人交互相结合来增强后者的安全性将是未来发展的方向。

面向人机协作中的安全物理交互仿真分析

以协作机器人Baxter为研究对象,面向人机协作中安全物理交互问题,提出利用Simscape Multibody工具箱建立协作机器人Baxter与人进行物理交互的仿真环境。针对人机协作任务中的安全物理交互问题,建立线性弹簧-阻尼系统用于模拟和计算人机协同任务过程中的接触力;利用计算力矩控制器和机器人逆动力学前馈实现机器人的碰撞检测,并通过安全跟踪关节运动轨迹实现机器人模型与人体模型的安全物理交互过程;然后利用ROS Toolbox工具箱实现与Baxter机器人之间的通信,直接进行实验测试。实验结果显示,在仿真中结合碰撞检测和安全轨迹跟踪算法机器人末端与障碍物发生碰撞后会停止运动,在受到与机器人运动方向相反的外力时表现出顺应性,对机器人在进行实际物理交互时其安全和交互能力有明显的提高。可见安全物理交互仿真分析对实际人机协作任务的参考意义。

基于串联离合驱动的安全机械手静力学分析

以物理人机交互安全为背景,提出了基于串联离合驱动的安全机械手静力学分析方法。以末端执行器为参考点经过一系列推导得到关节限制力矩和最大各向同性力及最大接触力之间的关系及运算步骤,并以最大各向同性力与最大接触力的比值作为安全性能指标,应用于冗余机械手运动学中以优化机械手的操作性能。将末端执行器表面网格化,以平行四边形单元为研究对象推导得到单元上最大接触力的表达式。最后通过数值实例验证上述两种静力学分析方法的一致性、简便性和有效性,为根据机器人结构位形对力矩限制器进行标定提供了控制理论基础。

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

Passivity-based controllers are widely used to facilitate physical interaction between humans and elastic joint robots,as they enhance the stability of the interaction system.However,the joint position tracking performance can be limited by the structures of these controllers when the system is faced with uncertainties and rough high-order system state measurements(such as joint accelerations and jerks).This study presents a variable structure passivity(VSP)control method for joint position tracking of elastic joint robots,which combines the advantages of passive control and variable structure control.This method ensures the tracking error converges in a finite time,even when the system faces uncertainties.The method also preserves the passivity of the system.Moreover,a cascaded observer,called CHOSSO,is also proposed to accurately estimate high-order system states,relying only on position and velocity signals.This observer allows independent implementation of disturbance compensation in the acceleration and jerk estimation channels.In particular,the observer has an enhanced ability to handle fast time-varying disturbances in physical human-robot interaction.The effectiveness of the proposed method is verified through simulations and experiments on a lower limb rehabilitation robot equipped with elastic joints.

An EMG-Based Biomimetic Variable Stiffness Modulation Strategy for Bilateral Motor Skills Relearning of Upper Limb Elbow Joint Rehabilitation

Bilateral rehabilitation systems with bilateral or unilateral assistive robots have been developed for hemiplegia patients to recover their one-side paralysis.However,the compliant robotic assistance to promote bilateral inter-limb coordination remains a challenge that should be addressed.In this paper,a biomimetic variable stiffness modulation strategy for the Variable Stiffness Actuator(VSA)integrated robotic is proposed to improve bilateral limb coordination and promote bilateral motor skills relearning.An Electromyography(EMG)-driven synergy reference stiffness estimation model of the upper limb elbow joint is developed to reproduce the muscle synergy effect on the affected side limb by independent real-time stiffness control.Additionally,the bilateral impedance control is incorporated for realizing compliant patient-robot interaction.Preliminary experiments were carried out to evaluate the tracking performance and investigate the multiple task intensities’influence on bilateral motor skills relearning.Experimental results evidence the proposed method could enable bilateral motor task skills relearning with wide-range task intensities and further promote bilateral inter-limb coordination.

Design and Analysis of a 2-DOF Actuator with Variable Stiffness Based on Leaf Springs

Variable Stiffness Actuator(VSA)is the core mechanism to achieve physical human–robot interaction,which is an inevitable development trend in robotic.The existing variable stiffness actuators are basically single degree-of-freedom(DOF)rotating joints,which are achieving multi-DOF motion by cascades and resulting in complex robot body structures.In this paper,an integrated 2-DOF actuator with variable stiffness is proposed,which could be used for bionic wrist joints or shoulder joints.The 2-DOF motion is coupling in one universal joint,which is different from the way of single DOF actuators cascade.Based on the 2-DOF orthogonal motion generated by the spherical wrist parallel mechanism,the stiffness could be adjusted by varying the effective length of the springs,which is uniformly distributed in the variable stiffness unit.The variable stiffness principle,the model design,and theoretical analysis of the VSA are discussed in this work.The independence of adjusting the equilibrium position and stiffness of the actuator is validated by experiments.The results show that the measured actuator characteristics are sufficiently matched the theoretical values.In the future,VSA could be used in biped robot or robotic arm,ensuring the safety of human–robot interaction.