scholarly journals A Piezoresistive Tactile Sensor for a Large Area Employing Neural Network

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 27 ◽  
Author(s):  
Youzhi Zhang ◽  
Jinhua Ye ◽  
Zhengkang Lin ◽  
Shuheng Huang ◽  
Haomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Indium Tin Oxide ◽  
Lower Layer ◽  
Layer Structure ◽  
Rbf Neural Network ◽  
Tactile Sensor ◽  
Information Support ◽  
External Information ◽  
Large Area ◽  
Contact Position

Electronic skin is an important means through which robots can obtain external information. A novel flexible tactile sensor capable of simultaneously detecting the contact position and force was proposed in this paper. The tactile sensor had a three-layer structure. The upper layer was a specially designed conductive film based on indium-tin oxide polyethylene terephthalate (ITO-PET), which could be used for detecting contact position. The intermediate layer was a piezoresistive film used as the force-sensitive element. The lower layer was made of fully conductive material such as aluminum foil and was used only for signal output. In order to solve the inconsistencies and nonlinearity of the piezoresistive properties for large areas, a Radial Basis Function (RBF) neural network was used. This includes input, hidden, and output layers. The input layer has three nodes representing position coordinates, X, Y, and resistor, R. The output layer has one node representing force, F. A sensor sample was fabricated and experiments of contact position and force detection were performed on the sample. The results showed that the principal function of the tactile sensor was feasible. The sensor sample exhibited good consistency and linearity. The tactile sensor has only five lead wires and can provide the information support necessary for safe human—computer interactions.

RECOGNITION OF CONTACT STATE BY USING NEURAL NETWORK FOR MICROMACHINED ARRAY TYPE TACTILE SENSOR

2005 ◽  
Vol 02 (03) ◽  
pp. 181-190 ◽  
Author(s):  
SEIJI AOYAGI ◽  
TAKAAKI TANAKA ◽  
KENJI MAKIHIRA
Keyword(s):  
Neural Network ◽  
Rotation Angle ◽  
Simulated Data ◽  
Tactile Sensor ◽  
Training Data ◽  
Sensing Element ◽  
Array Type ◽  
Tactile Pattern ◽  
Hidden Layer ◽  
Contact Position

In this paper, a force sensing element having a pillar and a diaphragm is proposed and thereafter fabricated by micromachining. Piezo resistors are fabricated on a silicon diaphragm for detecting distortions caused by a force input to a pillar on the diaphragm. Since a practical arrayed sensor consisting of many of this element is still under development, the output of an assumed arrayed type tactile sensor is simulated by FEM (finite element method). Using simulated data, the possibility of tactile pattern recognition using a neural network (NN) is investigated. The learning method of NN, the number of units of the input layer and the hidden layer, as well as the number of training data are investigated for realizing high probability of recognition. The 14 subjects having different shape and size are recognized. This recognition succeeded even if the contact position and the rotation angle of these objects are changed.

Identify Steel Ball Surface Defect Based on Combination of Dynamic and Static RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 1000-1004 ◽  
Author(s):  
Yan Ling Zhao ◽  
Feng Ling Wu ◽  
Peng Wang ◽  
Jian Yi Zhang
Keyword(s):  
Neural Network ◽  
Surface Defect ◽  
Layer Structure ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Image Features ◽  
Steel Ball ◽  
Ball Surface ◽  
Defect Image ◽  
Hidden Layer

Steel ball, as a rolling body of all kinds of bearings, it direct affects the bearings precision, dynamic performance and service life. This paper introduces the digital image technology Radial Basis Function (RBF)-Neural network, based on extracting the Steel Ball surface defect image features, used the strategy which is combined with static- dynamic clustering to union the two-stage study and design the hidden layer structure. Simulation and experiment show that the RBF-neural network runs stably, has fast convergence and overall accuracy rate of 96%. These can meet the needs of practical application.

A position-sensitive electronic skin based on boundary potential projection theory

Sensor Review ◽  
2020 ◽  
Vol 40 (1) ◽  
pp. 130-140
Author(s):  
Youzhi Zhang ◽  
Zhengkang Lin ◽  
Xiaojun You ◽  
Xingping Huang ◽  
Jinhua Ye ◽  
...  
Keyword(s):  
Electric Fields ◽  
Tactile Sensor ◽  
Large Area ◽  
Content Type ◽  
Electronic Skin ◽  
Boundary Potential ◽  
Sensitive Sensor ◽  
Position Sensitive ◽  
Contact Position ◽  
Practical Implications

Purpose This paper aims to report a flexible position-sensitive sensor that can be applied as large-area electronic skin over the stiff media. Design/methodology/approach The sensor uses a whole piezoresistive film as a touch sensing area. By alternately constructing two uniform electric fields with orthogonal directions in the piezoresistive film, the local changes in conductivity caused by touch can be projected to the boundary along the equipotential line under the constraint of electric field. Based on the change of boundary potential in the two uniform electric fields, it can be easy to determine the position of the contact area in the piezoresistive film. Findings Experiment results show the proposed tactile sensor is capable of detecting the contact position and classifying the contact force in real-time based on the changes of the potential differences on the boundary of the sensor. Practical implications The application example of using the sensor sample as a controller in shooting game is presented in this paper. It shows that the sensor has excellent touch sensing performance. Originality/value In this paper, a position-sensitive electronic skin is proposed. The experiment results show that the sensor has great application prospects in the field of interactive tactile sensing.

scholarly journals Vision-Based Tactile Sensor Mechanism for the Estimation of Contact Position and Force Distribution Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1920
Author(s):  
Vijay Kakani ◽  
Xuenan Cui ◽  
Mingjie Ma ◽  
Hakil Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Tactile Sensor ◽  
Position Angle ◽  
Force Distribution ◽  
Labview Software ◽  
The Neural Network ◽  
Input Load ◽  
Contact Position ◽  
Pragmatic Aspects

This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force distribution. The current study also addresses pragmatic aspects, such as choice of the thickness and materials for the tactile fingertips and surface tendency, etc. The overall vision-based tactile sensor equipment interacts with an actuating motion controller, force gauge, and control PC (personal computer) with a LabVIEW software on it. The image acquisition was carried out using a compact stereo camera setup mounted inside the elastic body to observe and measure the amount of deformation by the motion and input load. The vision-based tactile sensor test bench was employed to collect the output contact position, angle, and force distribution caused by various randomly considered input loads for motion in X, Y, Z directions and RxRy rotational motion. The retrieved image information, contact position, area, and force distribution from different input loads with specified 3D position and angle are utilized for deep learning. A convolutional neural network VGG-16 classification modelhas been modified to a regression network model and transfer learning was applied to suit the regression task of estimating contact position and force distribution. Several experiments were carried out using thick and thin sized tactile sensors with various shapes, such as circle, square, hexagon, for better validation of the predicted contact position, contact area, and force distribution.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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