Target Classification Method of Tactile Perception Data with Deep Learning |
| |
| Authors: | Xingxing Zhang Shaobo Li Jing Yang Qiang Bai Yang Wang Mingming Shen Ruiqiang Pu Qisong Song |
| |
| Affiliation: | 1.State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; (X.Z.); (J.Y.); (Y.W.);2.School of Mechanical Engineering, Guizhou University, Guiyang 550025, China; (Q.B.); (M.S.); (R.P.); (Q.S.);3.School of Mechanical & Electrical Engineering, Guizhou Normal University, Guiyang 550025, China |
| |
| Abstract: | In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the continuous development of tactile sensors, typical machine-learning algorithms often cannot solve the problem of target classification of pure tactile data. Here, we propose a new model by combining a convolutional neural network and a residual network, named ResNet10-v1. We optimized the convolutional kernel, hyperparameters, and loss function of the model, and further improved the accuracy of target classification through the K-means clustering method. We verified the feasibility and effectiveness of the proposed method through a large number of experiments. We expect to further improve the generalization ability of this method and provide an important reference for the research in the field of tactile perception classification. |
| |
| Keywords: | tactile sensor tactile perception data ResNet target classification |
|
|
