Grasp force optimization in the design of an underactuated robotic hand

2007 ◽  
Author(s):  
Jasper Schuurmans ◽  
Richard Q van der Linde ◽  
Dick H Plettenburg ◽  
Frans CT van der Helm
Keyword(s):  
Robotic Hand ◽  
Force Optimization ◽  
Grasp Force

Optimal grasp force for robotic grasping and in-hand manipulation with impedance control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaoqing Li ◽  
Ziyu Chen ◽  
Chao Ma
Keyword(s):  
Degrees Of Freedom ◽  
Impedance Control ◽  
Difficult Problem ◽  
Contact Forces ◽  
Design Parameters ◽  
Robotic Hand ◽  
Content Type ◽  
Cone Constraint ◽  
Grasp Force ◽  
Grasp Quality

Purpose The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations. Design/methodology/approach To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function. Findings Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand. Originality/value This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

The Influence of the Abduction Joints of Four Fingers to Grasp: Experimental and Simulated Verification

2021 ◽  
Vol 11 (24) ◽  
pp. 11960
Author(s):  
Yadong Yan ◽  
Chang Cheng ◽  
Mingjun Guan ◽  
Jianan Zhang ◽  
Yu Wang
Keyword(s):  
Success Rate ◽  
Hand Function ◽  
Great Influence ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Robotic Hand ◽  
Metacarpophalangeal Joints ◽  
Flexion Extension ◽  
Grasp Force ◽  
Grasp Quality

The thumb is the most important finger of the human hand and has a great influence on grasp manipulations. However, the extent to which joints other than the thumb joints affect the grasp, and thus, which joints should be included in a prosthetic hand, remains an open issue. In this paper, we focus on the metacarpophalangeal joints of the four fingers, except the thumb, which can generate flexion/extension and abduction/adduction motions. The contribution of these joints to grasping was evaluated in four aspects: grasp size, grasp force, grasp quality and grasp success rate. Six subjects participated in experiments with respect to the maximum grasp size and grasp force. The results show that possessing abduction mobility of the metacarpophalangeal joints can increase the grasp size by 4.67 ± 1.93 mm and the grasp force by 5.27 ± 4.25 N. Then, the grasping quality and success rate were tested in a simulation platform and using a robotic hand, respectively. The results show that grasp quality was promoted by 76.7% in the simulated environment with abduction mobility compared to without abduction mobility, whereas the grasp success rate was promoted by 68.3%. We believe that the results of this work can benefit the understanding of hand function and prosthetic hand design.

scholarly journals Design of a fuzzy safety margin derivation system for grip force control of robotic hand in precision grasp task

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110180
Author(s):  
Canfer Islek ◽  
Ersin Ozdemir
Keyword(s):  
Grip Force ◽  
Human Performance ◽  
Fuzzy Logic Controller ◽  
Safety Margin ◽  
Surface Friction ◽  
Robotic Hand ◽  
Performance Tests ◽  
Human Beings ◽  
Limit Value ◽  
Grasp Force

In this study, the aim was to grasp and lift an unknown object without causing any permanent change on its shape using a robotic hand. When people lift objects, they add extra force for safety above the minimum limit value of the grasp force. This extra force is expressed as the “safety margin” in the literature. In the conducted study, the safety margin is minimized and the grasp force was controlled. For this purpose, the safety margin performance of human beings for object grasping was measured by the developed system. The obtained data were assessed for a fuzzy logic controller (FLC), and the fuzzy safety margin derivation system (SMDS) was designed. In the literature, the safety margin rate was reported to vary between 10% and 40%. To be the basis for this study, in the experimental study conducted to measure the grip performance of humans, safety margin ratios ranging from 9% to 20% for different surface friction properties and different weights were obtained. As a result of performance tests performed in Matlab/Simulink environment of FLC presented in this study, safety margin ratios ranging from 8% to 21% for different surface friction properties and weights were obtained. It was observed that the results of the performance tests of the developed system were very close to the data of human performance. The results obtained demonstrate that the designed fuzzy SMDS can be used safely in the control of the grasp force for the precise grasping task of a robot hand.

A simplified model of a multi-jointed mechanical finger calibrated with experimental data by vision system

2021 ◽  
Vol 235 (1) ◽  
pp. 164-175
Author(s):  
C Cosenza ◽  
V Niola ◽  
S Savino
Keyword(s):  
Experimental Data ◽  
Vision System ◽  
Control Strategies ◽  
Image Correlation ◽  
Robotic Hand ◽  
Prosthetic Device ◽  
Prosthetic Hands ◽  
Mechanical Hand ◽  
Free Mode ◽  
Mechanical Finger

The development of suitable models for mechanical fingers, whether they are part of prosthetic device or of a robotic hand, is a powerful tool to predict the behaviour of their components since the early stages of design, especially for underactuated mechanisms. Experimental data can improve the reliability of such models and promote their application to build proper control strategies especially for prosthetic hands. Here, we have developed a multi-jointed model of a mechanical finger. The finger is part of the Federica hand: an underactuated mechanical hand that was conceived for prosthetic purpose. The model accounts for friction phenomena in the finger and it is tuned with experimental data acquired through a digital image correlation device. The model allowed us to write kinematics relations of the phalanges and evaluate finger configurations in relation to the closure velocity. Moreover, it was possible to estimate the tendon force and the work analysis occurring during the closure tasks, both in free mode and in presence of objects.

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