RESEARCH ARTICLE

Function-oriented optimization design method for underactuated tendon-driven humanoid prosthetic hand

  • Yue ZHENG 1,2,3,4 ,
  • Xiangxin LI , 1,3,4 ,
  • Lan TIAN 1,2,3,4 ,
  • Guanglin LI , 1,2,3,4
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  • 1. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, China
  • 2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
  • 3. SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518055, China
  • 4. Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen 518055, China

Received date: 27 Oct 2021

Accepted date: 17 Apr 2022

Published date: 15 Sep 2022

Copyright

2022 Higher Education Press 2022

Abstract

The loss of hand functions in upper limb amputees severely restricts their mobility in daily life. Wearing a humanoid prosthetic hand would be an effective way of restoring lost hand functions. In a prosthetic hand design, replicating the natural and dexterous grasping functions with a few actuators remains a big challenge. In this study, a function-oriented optimization design (FOD) method is proposed for the design of a tendon-driven humanoid prosthetic hand. An optimization function of different functional conditions of full-phalanx contact, total contact force, and force isotropy was constructed based on the kinetostatic model of a prosthetic finger for the evaluation of grasping performance. Using a genetic algorithm, the optimal geometric parameters of the prosthetic finger could be determined for specific functional requirements. Optimal results reveal that the structure of the prosthetic finger is significantly different when designed for different functional requirements and grasping target sizes. A prosthetic finger was fabricated and tested with grasping experiments. The mean absolute percentage error between the theoretical value and the experimental result is less than 10%, demonstrating that the kinetostatic model of the prosthetic finger is effective and makes the FOD method possible. This study suggests that the FOD method enables the systematic evaluation of grasping performance for prosthetic hands in the design stage, which could improve the design efficiency and help prosthetic hands meet the design requirements.

Cite this article

Yue ZHENG , Xiangxin LI , Lan TIAN , Guanglin LI . Function-oriented optimization design method for underactuated tendon-driven humanoid prosthetic hand[J]. Frontiers of Mechanical Engineering, 2022 , 17(3) : 40 . DOI: 10.1007/s11465-022-0696-0

Nomenclature

Abbreviations
DIP Distal interphalangeal
DOF Degree of freedom
FOD Function-oriented optimization design
GA Genetic algorithm
IP Interphalangeal
MAPE Mean absolute percentage error
MCP Metacarpophalangeal
MOC Main optimization condition
PIP Proximal interphalangeal
Variables
dji Distance from joint j to the centre of mass of phalanx i
EI Optimization function index
Ej Experimental force values of phalanges (j = 1, 2, 3)
F a Actuation force of the prosthetic finger
F, Fi Phalanx force (i = 1, 2, 3)
Gi Gravity of the phalanx (i = 1, 2, 3)
I1, I2, I3 Index of full-phalanx contact, total contact force and force isotropy, respectively
Iimoc Index Ii is the main optimization condition
J Transformational matrix relates to the contact position and friction
k, kj Spring elasticity coefficient (j = 1, 2, 3)
L, Li Phalanx length (i = 1, 2, 3)
MAPEi Mean absolute percentage error of the phalanx (i = 1, 2, 3)
n Number of prosthetic phalanges
nT Total number of testing positions
P, Pi Contact position (i = 1, 2, 3)
r, ri Joint radius (i = 1, 2, 3)
R, Ri Transmission ratio (i = 1, 2, 3)
Ta Actuation torque of the prosthetic finger
Tj Theoretical force valus of the phalanx (j = 1, 2, 3)
t Transformational matrix relates to spring coefficients and joint angle
T Transformational matrix relates to the transmission ratio
W1, W2, W3 Weight distribution of index I1, I2, and I3, respectively
w Workspace of the prosthetic finger
θ, θi Joint angle (i = 1, 2, 3)
µ, µi Surface friction coefficient (i = 1, 2, 3)
ε, εi Phalanx thickness (i = 1, 2, 3)
ηi Correlation coefficient (i = 1, 2, 3)

Acknowledgements

This work was supported in part by the Key-Area Research and Development Program of Guangdong Province, China (Grant No. 2020B0909020004), the National Key R&D Program of China (Grant No. 2020YFC2007900), and the Shenzhen Science and Technology Program, China (Grant No. CJGJZD20200617103002006).
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