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面向上肢康复的双臂机器人仿治疗师交互控制

林高 王斐 张鑫

林高, 王斐, 张鑫. 面向上肢康复的双臂机器人仿治疗师交互控制. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c250599
引用本文: 林高, 王斐, 张鑫. 面向上肢康复的双臂机器人仿治疗师交互控制. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c250599
Lin Gao, Wang Fei, Zhang Xin. Therapist-like interaction control of a dual-arm robot for upper limb rehabilitation. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c250599
Citation: Lin Gao, Wang Fei, Zhang Xin. Therapist-like interaction control of a dual-arm robot for upper limb rehabilitation. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c250599

面向上肢康复的双臂机器人仿治疗师交互控制

doi: 10.16383/j.aas.c250599 cstr: 32138.14.j.aas.c250599
基金项目: 国家自然科学基金(62373087), 辽宁省应用基础研究计划(2025JH2/101300009), 辽宁省兴辽英才计划(XLYC24110114)资助
详细信息
    作者简介:

    林高:东北大学机器人科学与工程学院博士研究生. 主要研究方向为上肢康复机器人, 模式识别, 物理人机交互技术. E-mail: 2310767@stu.neu.edu.cn

    王斐:东北大学机器人科学与工程学院教授. 主要研究方向为人机交互和人机协助技术, 生机电融合感知与认知. 本文通信作者. E-mail: wangfei@mail.neu.edu.cn

    张鑫:沈阳市第一人民医院康复医学科副主任医师. 主要研究方向为急性脑血管病, 神经康复. E-mail: zhangxin0321sy@163.com

Therapist-like Interaction Control of a Dual-Arm Robot for Upper Limb Rehabilitation

Funds: Supported by National Natural Science Foundation of China (62373087), Liaoning Provincial Applied Basic Research Program (2025JH2/101300009), and Liaoning Revitalization Talents Program (XLYC24110114)
More Information
    Author Bio:

    LIN Gao Ph.D. candidate at the Faculty of Robot Science and Engineering, Northeastern University. His research interest include upper-limb rehabilitation robots, pattern recognition, and physical human–robot interaction

    WANG Fei Professor at the Faculty of Robot Science and Engineering, Northeastern University. His research interest include human–robot interaction and human–robot assistance technologies, as well as bio-electro-mechanical integrated perception and cognition. Corresponding author of this paper

    ZHANG Xin Associate Chief Physician at the Department of Rehabilitation Medicine, Shenyang First Peopl's Hospital. Her research interest include cerebrovascular diseases and neurorehabilitation

  • 摘要: 具有治疗师般个性化、柔顺且安全交互的康复机器人可有效防止二次伤害并提升康复效率. 本文提出一种基于双臂机器人的新型上肢康复框架, 用于实现机器人治疗师般的交互. 首先, 以安全性为首要目标, 建立七自由度上肢运动学模型, 用于评估上肢末端以及前臂后端的可达训练空间; 利用双臂康复的优势, 提出一种非冗余逆运动学方法以约束关节运动角度, 进而构建任务-关节双重约束下的安全机制. 其次, 考虑个性化与柔顺交互, 设计了一种基于势场的按需辅助控制策略, 使双臂机器人能够从单次演示中学习治疗师的个性化牵引特性, 并根据上肢的运动能力和训练参与度提供自适应柔顺辅助. 实验结果表明, 所提方法兼具末端牵引式康复机器人动作适应性高、接触少以及外骨骼式康复机器人精准空间训练的特点, 并能够根据上肢训练状态实施按需辅助. 随着双臂及人形机器人的应用越来越广泛, 所提出的方法为机器人在医院和家庭环境中实现治疗师般个性化、柔顺且安全的康复训练提供了一条新途径.
  • 图  1  当前两种上肢康复机器人与所提出受治疗师启发的双臂康复机器人. (a)末端牵引式康复机器人; (b)外骨骼式康复机器人; (c)双臂康复机器人

    Fig.  1  Current two types of upper-limb rehabilitation robots and the proposed therapist-inspired dual-arm rehabilitation robot. (a) End-effector rehabilitation robot; (b) Exoskeleton rehabilitation robot; (c) Dual-arm rehabilitation robot

    图  2  7-DOF上肢运动学模型

    Fig.  2  7-DOF upper-limb kinematic model

    图  3  牵引演示与演示学习流程

    Fig.  3  Traction demonstration and learning from demonstration process

    图  4  (a~d)训练动作1~3; (e)双臂康复机器人平台; (f) IMU和EMG传感器放置部位

    Fig.  4  (a~d) Training motions 1~3; (e) Dual-arm rehabilitation robot platform; (f) Placement locations of the IMU and EMG sensors

    图  5  参数选择与可达训练空间构建效果. (a~b)可达训练空间$ A_1 $、$ A_2 $体积与采样点数之间的关系;(c~d)受试者3蒙特卡洛$ E_1 $、$ E_2 $点集与可达训练空间$ A_1 $、$ A_2 $构建效果

    Fig.  5  Parameter selection and reachable training space construction effects. (a~b) Relationship between the volumes of reachable training spaces $A_1$ and $A_2$ and the number of sampling points; (c~d) Construction effects of the Monte Carlo point sets $E_1$ and $E_2$ and the reachable training spaces $A_1$ and $A_2$ for Subject 3

    图  6  所提非冗余和冗余关节空间约束效果对比

    Fig.  6  Comparison of the proposed non-redundant and redundant joint-space constraint effects

    图  7  不同参数影响下的势场可视化效果

    Fig.  7  Visualization effects of the potential field under different parameter settings

    图  8  (a)康复训练2下受试者3对应的势场可视化效果; (b)不同程度扰动下系统的柔顺约束效果

    Fig.  8  (a) Visualization effect of the potential field corresponding to Subject 3 during rehabilitation training 2; (b) Compliant constraint effects of the system under different levels of perturbation

    图  9  关节空间下所提方法与基线方法训练效果对比

    Fig.  9  Comparison of training performance in joint space between the proposed and baseline methods

    图  10  任务空间下所提方法与基线方法训练效果对比

    Fig.  10  Comparison of training performance in task space between the proposed and baseline methods

    图  11  三种不同模式下训练1-3对应的空间轨迹

    Fig.  11  Spatial trajectories corresponding to training motions 1–3 under three different modes

    图  12  三种不同模式下训练1-3的训练参与度与交互刚度之间的关系

    Fig.  12  Relationship between training participation and interaction stiffness for training motions 1–3 under three different modes

    表  1  上肢运动学模型D-H参数

    Table  1  D-H parameters of the upper limb kinematic model

    关节 $ \theta_{i}(^\circ) $ $ a_{i} $ $ d_{i} $ $ \alpha_{i}(^\circ) $ 范围(°)
    肩旋转 $ \theta_{1} $ 0 0 90 −90−80
    肩收展 $ \theta_{2} $−90 0 0 −90 0−90
    肩屈伸 $ \theta_{3} $ $ l_1 $ 0 0 −60−90
    肘屈伸 $ \theta_{4} $/$ \theta_{4} $+90 0/$ l_2' $ 0 −90 −90−45
    腕旋转 $ \theta_{5} $ 0 $ l_2 $/$ l_2'' $ −90 −90−0
    腕收展 $ \theta_{6} $−90 0 0 90 −80−45
    腕屈伸 $ \theta_{7} $ $ l_3 $ 0 0 −15−45
    下载: 导出CSV

    表  2  关节空间下所提方法与基线方法训练效果对比

    Table  2  Comparison of training performance between the proposed method and baseline methods in joint space

    关节空间 训练1 训练2 训练3
    双臂 基线 双臂 基线 双臂 基线
    RMSE PCC RMSE PCC RMSE PCC RMSE PCC RMSE PCC RMSE PCC
    肩上举 0.98 0.99 20.75 0.78 1.06 1.00 3.63 0.99 0.88 1.00 21.68 0.86
    肩内旋 0.99 0.99 11.24 0.85 0.97 0.99 2.44 0.97 1.31 0.95 22.26 −0.37
    肩屈曲 1.53 0.90 3.45 0.57 1.43 1.00 1.88 0.999 2.40 0.99 16.01 0.45
    肩外展 0.87 1.00 21.43 0.93 0.97 0.99 2.55 0.99 0.85 1.00 22.69 0.94
    肘屈曲 2.06 0.99 6.14 0.99 1.63 0.99 5.05 0.99 1.74 0.99 5.44 0.93
    腕伸展 1.12 0.99 5.11 0.53 1.43 0.92 6.98 0.22 1.25 0.99 8.47 0.66
    腕外展 1.07 0.67 12.31 0.05 1.14 0.78 1.95 0.54 0.83 0.83 14.31 0.20
    腕旋后 1.06 0.99 16.20 0.99 1.05 0.99 21.53 −0.92 0.93 0.99 25.78 0.99
    下载: 导出CSV

    表  3  任务空间下所提方法与基线方法训练效果对比

    Table  3  Comparison of training performance between the proposed method and baseline methods in task space

    任务空间 训练1 训练2 训练3
    双臂 基线 双臂 基线 双臂 基线
    RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE
    肘部 5.89 4.64 92.74 78.76 6.28 5.04 25.24 21.32 8.94 6.84 101.89 91.70
    腕部 6.75 5.52 46.85 38.43 8.88 6.98 32.87 28.46 9.87 7.87 54.60 47.19
    手部 6.73 5.48 39.61 31.91 8.32 6.61 68.56 53.99 9.81 8.09 62.58 56.05
    下载: 导出CSV
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  • 收稿日期:  2025-11-04
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