Design and experimental testing of a tactile sensor for self-compensation of contact error in soft tissue stiffness measurement

Erukainure, Frank Efe; Parque, Victor; Hassan, Mohsen A.; FathEl-Bab, Ahmed M. R.

doi:10.1007/s12206-022-0943-7

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Flexible tactile sensors have wide application prospects in intelligent robot skin, wearable devices, health care and human–machine interaction fields [ 5 , 6 ]. Flexible tactile sensors can be used as robot skin to help intelligent robots perceive the physical properties of objects in real time, such as object hardness [ 7 , 8 , 9 ], roughness [ 10 , 11 , 12 ], temperature [ 13 , 14 , 15 ], and so on, which can effectively improve the robot’s perception of the surrounding environment and distinguish different objects; this is in order to make quick judgments and correct decisions in human–machine interactions, and improve the efficiency of practical applications [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Wang

et al. 2023

Micromachines

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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.

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