An improved BP neural network-based calibration method for the capacitive flexible three-axis tactile sensor array

Flexible tactile sensing based on capacitive sensing has become a research hotspot in recent years because of its low energy consumption, high performance and wide application prospects. However, the axis error caused by the coupling deformation of the dielectric will seriously affect the accuracy of the sensor. In this paper, a capacitive flexible three-axis tactile sensor array is modelled and simulated, and a neural network-based calibrator for the three-axis sensor array is proposed, which can be used to calibrate the simulated measurement data. The simulation results show that even though the correlation coefficient of linear regression for each axis is very close to 1, the effect of dielectric nonlinear coupling distortion cannot be eliminated. The calibration method based on the neural network can effectively suppress the nonlinear coupling distortion of the dielectric, and reduce the measurement coupling rate of the sensor model from 26% to 1%. At the same time, in order to ensure the measurement accuracy and robustness of different units in the sensor array, the input layer of the calibrator is expanded, and the data set containing capacitance information and two-dimensional location information is used for training. The experimental results show that the proposed calibration method combining two-dimensional position information training accurately calibrates the capacitive flexible three-dimensional tactile sensor array.