Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array

Abstract

Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human–machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 object types, based on the tactile time sequence signals collected by the novel sensor array; the average accuracies of hardness and type recognition are 100% and 99.7%, respectively. To further verify the classification ability of the ResNet model for the tactile feature information detected by the sensor array, the Multilayer Perceptron (MLP), LeNet, Multi-Channel Deep Convolutional Neural Network (MCDCNN), and ENCODER models are built based on the same dataset used for the ResNet model. The average recognition accuracies of the 4hardness categories, based on those four models, are 93.6%, 98.3%, 93.3%, and 98.1%. Meanwhile, the average recognition accuracies of the 12 object types, based on the four models, are 94.7%, 98.9%, 85.0%, and 96.4%. All of the results demonstrate that the novel porous graphene tactile sensor array has excellent perceptual performance and the ResNet model can very effectively and precisely complete the hardness and type recognition of objects for the flexible tactile sensor array.

Intelligent flexible manipulator system based on flexible tactile sensing (IFMSFTS) for kiwifruit ripeness classification

“…29 Li et al predicted the stability through pressure distribution feedback, and combined with support vector machine (SVM), the accuracy reached 98% 30 ; Zhang et al integrated a manipulator with a tactile sensor to identify the firmness through perception, and combined with principal component analysis-support vector machine (PCA-SVM) classifier, the online recognition rate was 90% 31 ; Song et al identify the firmness and type of objects through perception, and combined with RN (Res Net), the accuracy of firmness and type identification can reach 100 and 99.7% respectively. 32 The manipulator system's essential component, the touch sensor, enables the manipulator to interact with its environment precisely, swiftly, and safely. 33 The application and advancement of tactile sensing is, however, constrained by the rigid properties, difficulty in bending, and high firmness of existing sensors, which are often made of silicon or other stiff semiconductor materials.…”

“…integrated a manipulator with a tactile sensor to identify the firmness through perception, and combined with principal component analysis‐support vector machine (PCA‐SVM) classifier, the online recognition rate was 90% 31 ; Song et al . identify the firmness and type of objects through perception, and combined with RN (Res Net), the accuracy of firmness and type identification can reach 100 and 99.7% respectively 32 . The manipulator system's essential component, the touch sensor, enables the manipulator to interact with its environment precisely, swiftly, and safely 33 .…”

High linear sensitivity humidity and temperature sensing based on chiral long-period fiber grating

“…Then, in 2023, Wang et al. utilized the thermal properties of graphene to fabricate a flexible thermoelectric temperature sensor with excellent flexibility and high temperature sensitivity 22 . As a result, there has been an increasing interest in employing graphene nanofilm for temperature sensing applications in recent years 14 , 21…”

“…As a result, there has been an increasing interest in employing graphene nanofilm for temperature sensing applications in recent years 14 , 21 – 23 …”

Gesture Recognition Based on a Convolutional Neural Network–Bidirectional Long Short-Term Memory Network for a Wearable Wrist Sensor with Multi-Walled Carbon Nanotube/Cotton Fabric Material

“…In recent years, the development of flexible wearable e-skin has spurred research on softness [1], comfort [2], breathability [3], biocompatibility [4,5], and stability [6]. To date, flexible pressure/strain sensors have been designed based on a variety of sensing mechanisms, including piezoelectric [7,8], capacitive [9,10], piezoresistive [11,12], and self-powered [13,14] sensing. Flexible pressure sensors based on the piezoresistive mechanism have high sensitivity and fast response time, making them widely used in detecting dynamically changing pressure signals [15].…”