Design and slip prevention control of a multi-sensory anthropomorphic prosthetic hand

Purpose The purpose of this paper is to design a multi-sensory anthropomorphic prosthetic hand and a grasping controller that can detect the slip and automatically adjust the grasping force to prevent the slip. Design/methodology/approach To improve the dexterity, sensing, controllability and practicability of a prosthetic hand, a modular and multi-sensory prosthetic hand was presented. In addition, a slip prevention control based on the tactile feedback was proposed to improve the grasp stability. The proposed controller identifies slippages through detecting the high-frequency vibration signal at the sliding surface in real time and the discrete wavelet transform (DWT) was used to extract the eigenvalues to identify slippages. Once the slip is detected, a direct-feedback method of adjusting the grasp force related with the sliding times was used to prevent it. Furthermore, the stiffness of different objects was estimated and used to improve the grasp force control. The performances of the stiffness estimation, slip detection and slip control are experimentally evaluated. Findings It was found from the experiment of stiffness estimation that the accuracy rate of identification of the hard metal bottle could reach to 90%, while the accuracy rate of identification of the plastic bottles could reach to 80%. There was a small misjudgment rate in the identification of hard and soft plastic bottles. The stiffness of soft plastic bottles, hard plastic bottles and metal bottles were 0.64 N/mm, 1.36 N/mm and 32.55 N/mm, respectively. The results of slip detection and control show that the proposed prosthetic hand with a slip prevention controller can fast and effectively detect and prevent the slip for different disturbances, which has a certain application prospect. Practical implications Due to the small size, low weight, high integration and modularity, the prosthetic hand is easily applied to upper-limb amputees. Meanwhile, the method of the slip prevention control can be used for upper-limb amputees to complete more tasks stably in daily lives. Originality/value A multi-sensory anthropomorphic prosthetic hand is designed, and a method of stable grasps control based on slip detection by a tactile sensor on the fingertip is proposed. The method combines the stiffness estimation of the object and the real-time slip detection based on DWT with the design of the proportion differentiation robust controller based on a disturbance observer and the force controller to achieve slip prevention and stable grasps. It is verified effectively by the experiments and is easy to be applied to commercial prostheses.

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

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.

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

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.

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

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.

Optimal strategy of sEMG feature and measurement position for grasp force estimation

Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it will increase the computational burden. In this paper, an approach based on analysis of variance (ANOVA) and generalized regression neural network (GRNN) for optimal measurement positions and features is proposed, with the purpose of using fewer measurement positions or features to achieve higher estimation accuracy. Firstly, we captured six channels of sEMG from subjects’ forearm and grasp force synchronously. Then, four kinds of features in time domain are extracted from each channel of sEMG. By combining different measurement position sets (MPSs) and feature set (FSs), we construct 945 data sets. These data sets are fed to GRNN to realize grasp force estimation. Normalized root mean square error (NRMS), normalized mean of absolute error (NMAE), and correlation coefficient (CC) between estimated grasp force and actual force are introduced to evaluate the performance of grasp force estimation. Finally, ANOVA and Tukey HSD testing are introduced to analyze grasp force estimation results so as to obtain the optimal measurement positions and features. We obtain the optimal MPSs for grasp force estimation when different FSs are employed, and the optimal FSs when different MPSs are utilized.

Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation

Surface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with daily activities which degrades the accuracy and reliability of force estimation from sEMG signals. Conventional qualitative measurements of muscle fatigue contribute to an improved force estimation model with limited progress. This paper proposes an easy-to-implement method to evaluate the muscle fatigue quantitatively and demonstrates that the proposed metrics can have a substantial impact on improving the performance of hand grasp force estimation. Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. Experiments are conducted in the three cases: (1) pooling training data from all muscle fatigue states with time-domain feature only, (2) employing frequency domain feature for expression of muscle fatigue information based on case 1, and 3) incorporating the quantitative metric of muscle fatigue value as an additional input for estimation model based on case 1. The results show that the degree of muscle fatigue and task intensity can be easily distinguished, and the additional input of muscle fatigue in BPNN greatly improves the performance of hand grasp force estimation, which is reflected by the 6.3797% increase in R2 (coefficient of determination) value.