Abstract
A flexible tactile sensor array with 6 × 6 N‐type sensitive elements made of conductive rubber is presented in this paper. The property and principle of the tactile sensor are analyzed in detail. Based on the piezoresistivity of conductive rubber, this paper takes full advantage of the nonlinear approximation ability of the radial basis function neural network (RBFNN) method to approach the high‐dimensional mapping relation between the resistance values of the N‐type sensitive element and the three‐dimensional (3D) force and to accomplish the accurate prediction of the magnitude of 3D force loaded on the sensor. In the prediction process, the k‐means algorithm and recursive least square (RLS) method are used to optimize the RBFNN, and the k‐fold cross‐validation method is conducted to build the training set and testing set to improve the prediction precision of the 3D force. The optimized RBFNN with different spreads is used to verify its influence on the performance of 3D force prediction, and the results indicate that the spread value plays a very important role in the prediction process. Then, sliding window technology is introduced to build the RBFNN model. Experimental results show that setting the size of the sliding window appropriately can effectively reduce the prediction error of the 3D force exerted on the sensor and improve the performance of the RBFNN predictor, which means that the sliding window technology is very feasible and valid in 3D force prediction for the flexible tactile sensor. All of the results indicate that the optimized RBFNN with high robustness can be well applied to the 3D force prediction research of the flexible tactile sensor.
Highlights
With the rapid development of intelligent robot technology, researchers are eager to endow robots with a similar tactile perception of human skin to improve the robot’s ability of man-machine interaction
The N-type sensitive element is very sensitive to the tactile force and stress, and it is composed of three conductive columns which are made of the conductive rubber
Based on the piezoresistive effect of conductive rubber, this paper takes full advantage of the approximation ability of the radial basis function neural network (RBFNN) method to accomplish the accurate prediction of the magnitude of 3D force for the flexible tactile sensor and approach the high-dimensional mapping relation between the resistance values of the N-type sensitive element and the 3D force
Summary
With the rapid development of intelligent robot technology, researchers are eager to endow robots with a similar tactile perception of human skin to improve the robot’s ability of man-machine interaction. The research of flexible tactile sensors has become one of the hot topics in the intelligent robot skin field. The flexible tactile sensor is mainly developed based on the principle of capacitance [2,3,4,5], piezoelectric effect [6,7,8,9], and piezoresistive effect [10, 11]. Based on a 3 × 3 sparse flexible tactile sensor array, Liu et al [16] realized the detection of arbitrary contact force loaded on the sparse tactile sensor by utilizing the inverse solution method and the diffusion effect of the elastomer cover. Precise detection and prediction of contact force are important in the research of flexible tactile sensors.
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