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

doi:10.1007/s11465-022-0696-0

Frontiers of Mechanical Engineering

2022, Vol. 17

Issue (3): 40 https://doi.org/10.1007/s11465-022-0696-0

本期目录

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(

)

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

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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.

Key words： function-oriented tendon driven prosthetic hand optimization humanoid underactuated

收稿日期: 2021-10-27 出版日期: 2022-10-11

Corresponding Author(s): Xiangxin LI,Guanglin LI

引用本文:

. [J]. Frontiers of Mechanical Engineering, 2022, 17(3): 40.
Yue ZHENG, Xiangxin LI, Lan TIAN, Guanglin LI. Function-oriented optimization design method for underactuated tendon-driven humanoid prosthetic hand. Front. Mech. Eng., 2022, 17(3): 40.

链接本文:

https://academic.hep.com.cn/fme/CN/10.1007/s11465-022-0696-0
https://academic.hep.com.cn/fme/CN/Y2022/V17/I3/40

Fig.1

Parameter	Sign	Specific parameters	Numerical value
Phalanx length	L	$L = [L 1 L 2 L 3]$	$[40 0.67 L 1 0.62 L 1]$ mm
Contact position	P	$P = [P 1 P 2 P 3]$	$12 [L 1 L 2 L 3]$
Joint radius	r	$r = [r 1 r 2 r 3]$	$r 1 = 5 mm$
Transmission ratio	R	$R = [R 1 R 2]$	$0 < R i ? 1$
Spring elasticity coefficients	k	$k = [k 1 k 2 k 3]$	[8.10 2.62 0.60] N?mm/rad
Joint angle	$θ$	$θ = [θ 1 θ 2 θ 3]$	$0 ? θ i ? π / 2$
Actuation force	$F a$	$F a$	20 N
Phalanx force	F	$F = [F 1 F 2 F 3]$	Represent in Eq. (6)
Surface friction coefficient	$μ$	$μ = [μ 1 μ 2 μ 3]$	Based on the material
Phalanx thickness	$ε$	$ε = [ε 1 ε 2 ε 3]$	Based on the design structure

Tab.1

Fig.2

Fig.3

Fig.4

Fig.5

Index	Functional condition	Distribution of $W i$	Optimal $R 1$	Optimal $R 2$
$I 1$	Full-phalanx contact	$W 1$ = 1, $W 2$ = 0, $W 3$ = 0	0.25	0.10
$I 2$	Total contact force	$W 1$ = 0, $W 2$ = 1, $W 3$ = 0	1.00	1.00
$I 3$	Force isotropy	$W 1$ = 0, $W 2$ = 0, $W 3$ = 1	0.55	0.30

Tab.2

Fig.6

Fig.7

Region	MOC	$W i$ of MOC	$W i$ of other conditions	$R 1$	$R 2$	EI
1	$I 1 m o c$	$W 1$	$W 2 > W 3$	0.00	0.00	0.00
2	$I 1 m o c$	$W 1$	$W 3 > W 2$	0.30	0.10	0.75
3	$I 2 m o c$	$W 2$	$W 1 > W 3$	0.15	0.25	0.40
4	$I 3 m o c$	$W 3$	$W 1 > W 2$	0.55	0.30	0.75
5	$I 3 m o c$	$W 3$	$W 2 > W 1$	0.95	0.65	0.55
6	$I 2 m o c$	$W 2$	$W 3 > W 1$	1.00	1.00	0.70

Tab.3

Fig.8

Fig.9

Fig.10

Fig.11

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
$d j i$	Distance from joint $j$ to the centre of mass of phalanx $i$
EI	Optimization function index
$E j$	Experimental force values of phalanges (j = 1, 2, 3)
$F a$	Actuation force of the prosthetic finger
F, F_i	Phalanx force (i = 1, 2, 3)
G_i	Gravity of the phalanx (i = 1, 2, 3)
I₁, I₂, I₃	Index of full-phalanx contact, total contact force and force isotropy, respectively
I_imoc	Index I_i is the main optimization condition
J	Transformational matrix relates to the contact position and friction
k, k_j	Spring elasticity coefficient (j = 1, 2, 3)
L, L_i	Phalanx length (i = 1, 2, 3)
MAPE_i	Mean absolute percentage error of the phalanx (i = 1, 2, 3)
n	Number of prosthetic phalanges
n_T	Total number of testing positions
P, P_i	Contact position (i = 1, 2, 3)
r, r_i	Joint radius (i = 1, 2, 3)
R, R_i	Transmission ratio (i = 1, 2, 3)
T_a	Actuation torque of the prosthetic finger
T_j	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, θ_i	Joint angle (i = 1, 2, 3)
µ, µ_i	Surface friction coefficient (i = 1, 2, 3)
ε, ε_i	Phalanx thickness (i = 1, 2, 3)
η_i	Correlation coefficient (i = 1, 2, 3)

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