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踝关节外骨骼人机耦合动力学与助力性能分析

高钰清 靳葳 徐鉴 方虹斌

高钰清, 靳葳, 徐鉴, 方虹斌. 踝关节外骨骼人机耦合动力学与助力性能分析. 力学学报, 2022, 54(12): 1-17 doi: 10.6052/0459-1879-22-472
引用本文: 高钰清, 靳葳, 徐鉴, 方虹斌. 踝关节外骨骼人机耦合动力学与助力性能分析. 力学学报, 2022, 54(12): 1-17 doi: 10.6052/0459-1879-22-472
Gao Yuqing, Jin Wei, Xu Jian, Fang Hongbin. Human-machine coupling dynamics and assistance performance analysis of an ankle exoskeleton. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(12): 1-17 doi: 10.6052/0459-1879-22-472
Citation: Gao Yuqing, Jin Wei, Xu Jian, Fang Hongbin. Human-machine coupling dynamics and assistance performance analysis of an ankle exoskeleton. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(12): 1-17 doi: 10.6052/0459-1879-22-472

踝关节外骨骼人机耦合动力学与助力性能分析

doi: 10.6052/0459-1879-22-472
基金项目: 国家自然科学基金资助项目(11902078)
详细信息
    作者简介:

    方虹斌, 研究员, 主要研究方向: 仿生移动机器人、外骨骼人机协同动力学、折纸结构和折纸超材料和多体动力学与控制. E-mail: fanghongbin@fudan.edu.cn

  • 中图分类号: O313

HUMAN-MACHINE COUPLING DYNAMICS AND ASSISTANCE PERFORMANCE ANALYSIS OF AN ANKLE EXOSKELETON

  • 摘要: 踝关节在人体下肢运动过程中提供了最大的关节力矩, 因此在下肢增强型外骨骼的研究中, 踝关节外骨骼受到了重点关注. 穿戴外骨骼的人体的行走是典型的动力学问题, 但目前人机耦合动力学的相关研究还处于早期阶段. 本文以绳驱踝关节外骨骼为研究对象, 融合机器人正运动学方法和拉格朗日方程建立了考虑足−地交互力、人体关节力矩和外骨骼力矩的人−机耦合动力学模型. 模型中, 足−地交互力由Kelvin-Voigt模型结合库伦摩擦模型描述, 人体关节力矩由基于粒子群优化的PD控制生成, 外骨骼期望力矩由上层控制器依据人体步态周期确定. 通过基于模型的动力学仿真, 本文从人体踝关节角度、踝关节力矩、踝关节功率和踝关节做功多个角度系统分析了踝关节外骨骼对人体行走的助力效果. 研究表明, 在2.0 km/h到6.5 km/h的人体步行速度下, 穿戴外骨骼可以实现至少24.84%的人体踝关节平均力矩下降和至少24.69%的踝关节做功下降. 本文也开展了基于SCONE平台的肌肉骨骼建模和预测仿真. 仿真结果表明, 在3.6 km/h的步行速度下, 穿戴外骨骼可以有效降低比目鱼肌的激活度峰值, 并使肌电信号的RMS值下降了6.21%, 从而从生理学的角度证实了踝关节外骨骼的助力效果. 本文的结果进一步完善了人体下肢−外骨骼耦合系统的动力学建模和分析方法, 从动力学和生理学角度证实和解释了踝关节外骨骼对行走的助力机制, 也为今后下肢外骨骼的实验研究提供了理论支撑.

     

  • 图  1  人体−踝关节外骨骼耦合系统及人体下肢运动学模型

    Figure  1.  Human-ankle exoskeleton coupled system and the kinematic model of the human lower extremity

    1  人体−踝关节外骨骼耦合系统及人体下肢运动学模型 (续)

    1.  Human-ankle exoskeleton coupled system and the kinematic model of the human lower extremity (continued)

    图  2  人体−外骨骼耦合动力学模型

    Figure  2.  Dynamics model of the human-exoskeleton system

    图  3  步态阶段划分

    Figure  3.  Division of gait stages

    图  4  基于粒子群优化的人体关节力矩PD控制流程

    Figure  4.  Block diagram of the PSO-based PD controller for human joint torques

    图  5  踝关节外骨骼助力力矩曲线

    Figure  5.  Assistive torque curve of the ankle exoskeleton

    图  6  踝关节外骨骼期望力矩与实际力矩对比

    Figure  6.  Comparison of the actual torque with the desired torque of the ankle exoskeleton

    图  7  穿戴和未穿外骨骼情况下人体踝关节角度对比

    Figure  7.  A comparison of human ankle angles for the cases with and without exoskeleton

    图  8  穿戴和未穿外骨骼情况下人体踝关节力矩对比

    Figure  8.  A comparison of human ankle torques for the cases with and without exoskeleton

    图  9  穿戴和未穿外骨骼情况下人体踝关节功率对比

    Figure  9.  A comparison of the human ankle power for the cases with and without exoskeleton

    图  10  SCONE人体−外骨骼耦合系统的肌肉骨骼动力学模型

    Figure  10.  Musculoskeletal dynamic model of the human-exoskeleton coupled system in SCONE

    图  11  SCONE预测仿真结果

    Figure  11.  Simulation results of SCONE

    表  1  人体体节DH参数

    Table  1.   DH parameters of the human body segments

    ${i_{}}$${\alpha _{i - 1}}$${a_{i - 1}}$${d_i}$${\theta _i}$
    1000${\theta _{{\text{body}}}}$
    2000${\theta _{{\text{left\_hip}}}}$
    30${l_{{\text{left\_thigh}}}}$0${\theta _{{\text{left\_knee}}}}$
    40${l_{{\text{left\_shank}}}}$0${\theta _{{\text{left\_ankle}}}}$
    5000${\theta _{{\text{right\_hip}}}}$
    60${l_{{\text{right\_thigh}}}}$0${\theta _{{\text{right\_knee}}}}$
    70${l_{{\text{right\_shank}}}}$0${\theta _{{\text{right\_ankle}}}}$
    下载: 导出CSV

    表  2  人体节段长度和重量值

    Table  2.   Lengths and weights of human segments

    ParametersValues/mParametersValues/kg
    ${l_{{\text{body}}}}$0.3${m_{{\text{body}}}}$20.0
    ${l_{{\text{thigh}}}}$0.5${m_{{\text{thigh}}}}$7.0
    ${l_{{\text{shank}}}}$0.4${m_{{\text{shank}}}}$3.0
    ${l_{{\text{foot}}}}$0.25${m_{{\text{foot}}}}$1.0
    下载: 导出CSV

    表  3  粒子群优化算法参数值

    Table  3.   Parameters of the PSO Algorithm

    ParametersValuesParametersValues
    ${D_{}}$14${\omega _{}}$1
    ${m_{}}$100${c_1}$2
    ${n_{}}$50${c_2}$2
    下载: 导出CSV

    表  4  助力曲线的参数值

    Table  4.   Parameters of the assistance curve

    ParametersValuesParametersValues
    ${a_1}$−0.0025${a_2}$−0.0373
    ${b_1}$0.1978${b_2}$5.8800
    ${c_1}$−2.6865${c_2}$−302.4
    ${d_1}$10.1093${d_2}$5040
    下载: 导出CSV

    表  5  不同行走速度下外骨骼助力效果评估

    Table  5.   Evaluation of exoskeleton assistance effect under different walking speed

    No.Walking speed/
    (km·h−1)
    Average peak torque reductionWork reduction
    leftrightleftright
    12.040.76%40.68%37.47%31.85%
    22.548.29%33.92%42.29%32.78%
    33.543.74%30.51%37.00%34.55%
    44.537.41%30.17%33.72%28.87%
    55.536.96%24.84%33.70%24.69%
    66.531.89%31.11%30.01%28.96%
    下载: 导出CSV

    表  6  SCONE中目标函数的权重值

    Table  6.   Weights of the objective function in SCONE

    WeightsValues
    ${w_{\text{g}}}$100
    ${w_{\text{e}}}$0.1
    ${w_{{\text{rf}}}}$10
    ${w_{{\text{lim\_a}}}}$0.1
    ${w_{{\text{lim\_k}}}}$0.1
    下载: 导出CSV
  • [1] 国家统计局, 国务院第七次全国人口普查领导小组办公室. 第七次全国人口普查公报(第五号). http://www.stats.gov.cn/tjsj/tjgb/rkpcgb/qgrkpcgb/202106/t20210628_1818824.html
    [2] 王存金, 董林杰, 李杰等. 基于人行走能耗分析的踝关节外骨骼设计. 机械工程学报, 2021, 57(19): 79-92 (Wang Cunjin, Dong Linjie, Li Jie, et al. Design of ankle exoskeleton based on analysis on energy cost of human walking. Journal of Mechanical Engineering, 2021, 57(19): 79-92 (in Chinese) doi: 10.3901/JME.2021.19.008
    [3] 韩亚丽, 王兴松. 人体行走下肢生物力学研究. 中国科学:技术科学, 2011, 41(5): 592-601 (Han Yali, Wang Xingsong. The biomechanical study of lower limb during human walking. Science in China:Technology Science, 2011, 41(5): 592-601 (in Chinese)
    [4] Collins SH, Wiggin MB, Sawicki GS. Reducing the energy cost of human walking using an unpowered exoskeleton. Nature, 2015, 522(7555): 212-215 doi: 10.1038/nature14288
    [5] Yandell MB, Tacca JR, Zelik KE. Design of a low profile, unpowered ankle exoskeleton that fits under clothes: overcoming practical barriers to widespread societal adoption. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(4): 712-723 doi: 10.1109/TNSRE.2019.2904924
    [6] Asbeck AT, De Rossi SMM, Holt KG, et al. A biologically inspired soft exosuit for walking assistance. The International Journal of Robotics Research, 2015, 34(6): 744-762 doi: 10.1177/0278364914562476
    [7] Bae J, Siviy C, Rouleau M, et al. A lightweight and efficient portable soft exosuit for paretic ankle assistance in walking after stroke//2018 IEEE International Conference on Robotics and Automation (ICRA), 2018: 2820-2827
    [8] Kim J, Quinlivan BT, Deprey LA, et al. Reducing the energy cost of walking with low assistance levels through optimized hip flexion assistance from a soft exosuit. Scientific Reports, 2022, 12(1): 1-13 doi: 10.1038/s41598-021-99269-x
    [9] Shepertycky M, Burton S, Dickson A, et al. Removing energy with an exoskeleton reduces the metabolic cost of walking. Science, 2021, 372(6545): 957-960 doi: 10.1126/science.aba9947
    [10] Awad LN, Kudzia P, Revi DA, et al. Walking faster and farther with a soft robotic exosuit: Implications for post-stroke gait assistance and rehabilitation. IEEE Open Journal of Engineering in Medicine and Biology, 2020, 1: 108-115 doi: 10.1109/OJEMB.2020.2984429
    [11] Witte KA, Fiers P, Sheets-Singer AL, et al. Improving the energy economy of human running with powered and unpowered ankle exoskeleton assistance. Science Robotics, 2020, 5(40): eaay9108 doi: 10.1126/scirobotics.aay9108
    [12] Barazesh H, Sharbafi MA. A biarticular passive exosuit to support balance control can reduce metabolic cost of walking. Bioinspiration & Biomimetics, 2020, 15(3): 036009
    [13] Asbeck AT, Schmidt K, Walsh CJ. Soft exosuit for hip assistance. Robotics and Autonomous Systems, 2015, 73: 102-110 doi: 10.1016/j.robot.2014.09.025
    [14] Hu H, Fang K, Guan H, et al. A novel control method of a soft exosuit with plantar pressure sensors//2019 IEEE 4 th International Conference on Advanced Robotics and Mechatronics (ICARM), 2019: 581-586
    [15] 葛一敏, 袁海辉, 甘春标. 基于步态切换的欠驱动双足机器人控制方法. 力学学报, 2018, 50(4): 871-879 (Ge Yimin, Yuan Haihui, Gan Chunbiao. Control method of an underactuated biped robot based on gait transition. Chinese Journal of Theoretical and Applied Mechanics, 2018, 50(4): 871-879 (in Chinese) doi: 10.6052/0459-1879-18-049
    [16] 方五益, 郭晛, 黎亮等. 柔性铰柔性杆机器人动力学建模、仿真和控制. 力学学报, 2020, 52(4): 965-974 ((Fang Wuyi, Guo Xian, Li Liang, et al. Dynamics modeling, simulation, and control of robots with flexible joints and flexible links. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(4): 965-974 (in Chinese) doi: 10.6052/0459-1879-20-067
    [17] Ezati M, Ghannadi B, McPhee J. A review of simulation methods for human movement dynamics with emphasis on gait. Multibody System Dynamics, 2019, 47(3): 265-292 doi: 10.1007/s11044-019-09685-1
    [18] Forner-Cordero A, Koopman H, Van der Helm FCT. Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles. Gait & Posture, 2006, 23(2): 189-199
    [19] Ren L, Jones RK, Howard D. Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. Journal of Biomechanics, 2008, 41(12): 2750-2759 doi: 10.1016/j.jbiomech.2008.06.001
    [20] Porsa S, Lin YC, Pandy MG. Direct methods for predicting movement biomechanics based upon optimal control theory with implementation in OpenSim. Annals of Biomedical Engineering, 2016, 44(8): 2542-2557 doi: 10.1007/s10439-015-1538-6
    [21] Lin YC, Walter JP, Pandy MG. Predictive simulations of neuromuscular coordination and joint-contact loading in human gait. Annals of Biomedical Engineering, 2018, 46(8): 1216-1227 doi: 10.1007/s10439-018-2026-6
    [22] Martin AE, Schmiedeler JP. Predicting human walking gaits with a simple planar model. Journal of Biomechanics, 2014, 47(6): 1416-1421 doi: 10.1016/j.jbiomech.2014.01.035
    [23] Davy DT, Audu ML. A dynamic optimization technique for predicting muscle forces in the swing phase of gait. Journal of Biomechanics, 1987, 20(2): 187-201 doi: 10.1016/0021-9290(87)90310-1
    [24] Farahani SD, Svinin M, Andersen MS, et al. Prediction of closed-chain human arm dynamics in a crank-rotation task. Journal of Biomechanics, 2016, 49(13): 2684-2693 doi: 10.1016/j.jbiomech.2016.05.034
    [25] Wehner M, Quinlivan B, Aubin PM, et al. A lightweight soft exosuit for gait assistance//2013 IEEE International Conference on Robotics and Automation (ICRA), 2013: 3362-3369
    [26] Sawicki GS, Beck ON, Kang I, et al. The exoskeleton expansion: improving walking and running economy. Journal of Neuroengineering and Rehabilitation, 2020, 17(1): 1-9 doi: 10.1186/s12984-019-0634-5
    [27] Machado M, Moreira P, Flores P, et al. Compliant contact force models in multibody dynamics: Evolution of the Hertz contact theory. Mechanism and Machine Theory, 2012, 53: 99-121 doi: 10.1016/j.mechmachtheory.2012.02.010
    [28] Carvalho AS, Martins JM. Exact restitution and generalizations for the Hunt–Crossley contact model. Mechanism and Machine Theory, 2019, 139: 174-194 doi: 10.1016/j.mechmachtheory.2019.03.028
    [29] 吕阳, 方虹斌, 徐鉴等. 四连杆膝关节假肢的动力学建模与分析. 力学学报, 2020, 52(4): 1157-1173 (Lü Yang, Fang Hongbin, Xu Jian, et al. Dynamic modeling and analysis of the lower limb prosthesis with four-bar linkage prosthetic knee. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(4): 1157-1173 (in Chinese) doi: 10.6052/0459-1879-20-048
    [30] Mostaghel N, Davis T. Representations of Coulomb friction for dynamic analysis. Earthquake Engineering & Structural Dynamics, 1997, 26(5): 541-548
    [31] Romano RA, Garcia C. Karnopp friction model identification for a real control valve. IFAC Proceedings Volumes, 2008, 41(2): 14906-14911 doi: 10.3182/20080706-5-KR-1001.02523
    [32] Marton L, Lantos B. Modeling, identification, and compensationof stick-slip friction. IEEE Transactions on Industrial Electronics, 2007, 54(1): 511-521 doi: 10.1109/TIE.2006.888804
    [33] Kamenar E, Zelenika S. Nanometric positioning accuracy in thepresence of presliding and sliding friction: modelling, identification and compensation. Mechanics Based Design of Structures and Machines, 2017, 45(1): 111-126 doi: 10.1080/15397734.2016.1149487
    [34] Geilinger M, Hahn D, Zehnder J, et al. Add: Analytically differentiable dynamics for multi-body systems with frictional contact. ACM Transactions on Graphics (TOG) , 2020, 39(6): 1-15
    [35] Zheng XD, Wang Q. LCP method for a planar passive dynamic walker based on an event-driven scheme. Acta Mechanica Sinica, 2018, 34(3): 578-588 doi: 10.1007/s10409-018-0749-0
    [36] 郑鹏, 王琪, 吕敬等. 摩擦与滚阻对被动行走器步态影响的研究. 力学学报, 2020, 52(1): 162-170 (Zheng Peng, Wang Qi, Lü Jing, et al. Study on the influence of friction and rolling resistance on the gait of passive dynamic walker. Chinese Journal of Theoretical and Applied Mechanics, 2020, 52(1): 162-170 (in Chinese) doi: 10.6052/0459-1879-19-216
    [37] 段文杰, 王琪, 王天舒. 圆弧足被动行走器非光滑动力学仿真研究. 力学学报, 2011, 43(4): 765-774 (Duan Wenjie, Wang Qi, Wang Tianshu. Simulation research of a passive dynamic walker with round feet based on non-smooth method. Chinese Journal of Theoretical and Applied Mechanics, 2011, 43(4): 765-774 (in Chinese) doi: 10.6052/0459-1879-2011-4-lxxb2010-277
    [38] Liu J, Fang H, Xu J. Online adaptive PID control for a multi-joint lower extremity exoskeleton system using improved particle swarm optimization. Machines, 2021, 10(1): 21 doi: 10.3390/machines10010021
    [39] 徐声. 踝关节外骨骼的人机协作控制策略及优化. [硕士论文]. 武汉: 武汉理工大学, 2019

    Xu Sheng. Human-machine cooperation control strategy and optimization of ankle exoskeleton. [Master Thesis]. Wuhan: Wuhan University Of Technology, 2019 (in Chinese))
    [40] Whittle MW. Gait Analysis: An Introduction. Butterworth-Heinemann, 2014
    [41] Girod B, Rabenstein R, Stenger A. Signals and Systems. John Wiley & Sons Incorporated, 2001
    [42] Rosner B, Glynn RJ, Ting Lee ML. Incorporation of clustering effects for the Wilcoxon rank sum test: a large-sample approach. Biometrics, 2003, 59(4): 1089-1098 doi: 10.1111/j.0006-341X.2003.00125.x
    [43] Geijtenbeek T. Scone: Open source software for predictive simulation of biological motion. Journal of Open Source Software, 2019, 4(38): 1421 doi: 10.21105/joss.01421
    [44] 范光辉. 基于人体下肢表面肌电信号的动作模式识别及疲劳度分析. [硕士论文]. 安徽: 安徽工业大学, 2020

    Fan Guanghui. Movement pattern recognition and fatigue analysis based on EMG signals from lower extremity surface of human body. [Master Thesis]. Anhui: Anhui University of Technology, 2020 (in Chinese)
    [45] 康乐. 人体下肢表面肌电信号的特性研究. [硕士论文]. 天津: 天津科技大学, 2012

    Kang Le. Research on the surface electromyography characteristics of human lower limb. [Master Thesis]. Tianjin: Tianjin University of Science and Technology, 2012 (in Chinese)
    [46] 马渊源. 足背屈运动疲劳前后胫骨前肌和比目鱼肌sEMG的变化特征. [硕士论文]. 苏州: 苏州大学, 2019

    Ma Yuanyuan. Changes of sEMG of tibialis anterior and soleus muscle before and after dorsiflexion induced fatigue. [Master’ s Thesis]. Suzhou: Soochow University, 2019 (in Chinese)
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  • 收稿日期:  2022-10-04
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