Human-Robot Interaction Evaluation-Based AAN Control for Upper Limb Rehabilitation Robots Driven by Series Elastic Actuators

被引:17
|
作者
Han, Shuaishuai [1 ,2 ]
Wang, Haoping [1 ]
Yu, Haoyong [2 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Peoples R China
[2] Natl Univ Singapore, Dept Biomed Engn, Singapore 117583, Singapore
基金
中国国家自然科学基金;
关键词
Assist-as-needed (AAN); human-robot interaction; impedance adaption; series elastic actuator (SEA)-driven robot; CONTROL DESIGN; FORCE CONTROL; AIDED NEUROREHABILITATION; EXOSKELETON; COMPLIANT; STRATEGIES; FEEDBACK;
D O I
10.1109/TRO.2023.3286073
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Series elastic actuators (SEAs) have been the most popular compliant actuators as they possess a variety of advantages, such as high compliance, good backdrivability, and tolerance to shocks. They have been adopted by various rehabilitation robots to provide appropriate assistance with suitable compliance during human-robot interaction. For a multijoint SEA-driven rehabilitation robot, a big challenge is to develop an assist-as-needed (AAN) method without losing stability during uncertain physical human-robot interaction. For this purpose, this article proposes a human-robot interaction evaluation-based AAN method for upper limb rehabilitation robots driven by SEAs. First, in order to stabilize the SEA-level dynamics, singular perturbation theory is adopted to design a fast time-scale controller. Second, for the robot-level dynamics, an iterative learning algorithm is adopted for impedance adaption according to the task performance and human intention. The interaction force feedback is introduced for human-robot interaction evaluation, and the intensity of robotic assistance will be adjusted periodically according to the evaluation results. The stability of human-robot interaction is provided with the Lyapunov method. Finally, the proposed rehabilitation method is constructed and implemented on a two-degree-of-freedom SEA-driven robot. It handles the uncertain interaction in such a principle that correct movements will lead to less assistance for encouraging participation and incorrect movements will lead to more assistance for effective training. The proposed method adapts to the subject's intention and encourages higher participation by decreasing impedance learning strength and increasing allowable motion error. It can fit the participants with different motor capabilities and provide adaptive assistance when a specific trainee tries to change his/her participation during rehabilitation. The performance of the AAN method was validated with experimental studies involving healthy subjects.
引用
收藏
页码:3437 / 3451
页数:15
相关论文
共 50 条
  • [31] Emotionally Driven Robot Control Architecture for Human-Robot Interaction
    Novikova, Jekaterina
    Gaudl, Swen
    Bryson, Joanna
    TOWARDS AUTONOMOUS ROBOTIC SYSTEMS, 2014, 8069 : 261 - 263
  • [32] Improving Human-Robot Interaction Safety through Compliant Motion Constraints in Bilateral Upper Limb Rehabilitation
    Miao, Qing
    Sun, Chenyang
    Zhong, Bin
    Guo, Kaiqi
    Zhang, Mingming
    2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE-ROBIO 2021), 2021, : 379 - 385
  • [33] Human-Robot Interaction Based on Gestures for Service Robots
    de Sousa, Patrick
    Esteves, Tiago
    Campos, Daniel
    Duarte, Fabio
    Santos, Joana
    Leao, Joao
    Xavier, Jose
    de Matos, Luis
    Camarneiro, Manuel
    Penas, Marcelo
    Miranda, Maria
    Silva, Ricardo
    Neves, Antonio J. R.
    Teixeira, Luis
    VIPIMAGE 2017, 2018, 27 : 700 - 709
  • [34] Adaptive Gait Training of a Lower Limb Rehabilitation Robot Based on Human-Robot Interaction Force Measurement
    Yu, Fuyang
    Liu, Yu
    Wu, Zhengxing
    Tan, Min
    Yu, Junzhi
    CYBORG AND BIONIC SYSTEMS, 2024, 5
  • [35] Force Control of Series Elastic Actuators-Driven Parallel Robot
    Lee, Hyunwook
    Kwak, Suhui
    Oh, Sehoon
    2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2018, : 5401 - 5406
  • [36] Adaptive Estimation of Human-Robot Interaction Force for Lower Limb Rehabilitation
    Liang, Xu
    Wang, Weiqun
    Hou, Zengguang
    Ren, Shixin
    Wang, Jiaxing
    Shi, Weiguo
    Su, Tingting
    NEURAL INFORMATION PROCESSING (ICONIP 2019), PT IV, 2019, 1142 : 540 - 547
  • [37] Safe physical human-robot interaction of mobility assistance robots: evaluation index and control
    Pervez, Aslam
    Ryu, Jeha
    ROBOTICA, 2011, 29 : 767 - 785
  • [38] Review: Intelligent control and human-robot interaction for collaborative robots
    Huang, Hai-Feng
    Liu, Pei-Sen
    Li, Qing
    Yu, Xin-Bo
    Gongcheng Kexue Xuebao/Chinese Journal of Engineering, 2022, 44 (04): : 780 - 791
  • [39] Position/force evaluation-based assist-as-needed control strategy design for upper limb rehabilitation exoskeleton
    Guo, Yida
    Wang, Haoping
    Tian, Yang
    Xu, Jiazhen
    Neural Computing and Applications, 2022, 34 (15) : 13075 - 13090
  • [40] Position/force evaluation-based assist-as-needed control strategy design for upper limb rehabilitation exoskeleton
    Guo, Yida
    Wang, Haoping
    Tian, Yang
    Xu, Jiazhen
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (15): : 13075 - 13090