Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor

Yang Song,Yanan Jiang,Salvatore Pirozzi,Feilu Wang,Rungen Ye,Shanna Lv,Mingkun Li,Yufeng Chen

doi:10.1155/2021/3667833

Abstract

To measure three-dimensional (3D) forces efficiently and improve the sensitivity of tactile sensors, a novel piezoelectric tactile sensor with a “sandwich” structure is proposed in this paper. An array of circular truncated cone-shaped sensitive units made of polyvinylidene fluoride (PVDF) is sandwiched between two flexible substrates of polydimethylsiloxane (PDMS). Based on the piezoelectric properties of the PVD F sensitive units, finite element modelling and analysis are carried out for the sensor. The relation between the force and the voltage of the sensitive unit is obtained, and a tactile perception model is established. The model can distinguish the sliding direction and identify the material of the slider loaded on the sensor. A backpropagation neural network (BPNN) algorithm is built to predict the 3D forces applied on the tactile sensor model, and the 3D forces are decoupled from the voltages of the sensitive units. The BPNN is further optimized by a genetic algorithm (GA) to improve the accuracy of the 3D force prediction, and fairly good prediction results are obtained. The experimental results show that the novel tactile sensor model can effectively predict the 3D forces, and the BPNN model optimized by the GA can predict the 3D forces with much higher precision, which also improves the intelligence of the sensor. All the prediction results indicate that the BPNN algorithm has very efficient performance in 3D force prediction for the piezoelectric tactile sensor.

Highlights

Obtaining useful tactile information from complex environment is important to the development of robot technology
The experimental results show that a genetic algorithm (GA) method can improve the approximation ability of backpropagation neural network (BPNN) effectively, improve the prediction accuracy of 3D force loaded on the sensor, and be well applied to the research of 3D force prediction for the piezoelectric tactile sensor
A flexible tactile sensor based on the piezoelectric polymer polyvinylidene fluoride (PVDF) is proposed

Summary

Introduction

Obtaining useful tactile information from complex environment is important to the development of robot technology. A lot of researchers focused on the materials and the structures of the tactile sensors, and the research of multidimensional and multifunctional flexible tactile sensors based on neural network methods has gradually become the focus in electronic skin field. Liu et al [18] presented a novel flexible capacitive 3D tactile sensor using crossbar PDMS walls and micropillar arrays as a composite structure dielectric layer for the sensor, which shows great potential for applications in robot technology. Chen et al [26] proposed a pressure sensor with nanowires/graphene heterostructures for static measurements based on the synergistic mechanisms between strain-induced polarization charges in piezoelectric nanowires, which shows great potential in the application of the electronic skin. Lee et al [27] prepared a multimodal electronic skin, and it can identify and distinguish mechanical stresses from a single stimulus such as pressure and tensile strain and those from a combination of multiple stimuli

Results

Discussion

Conclusion