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.
Highlights
Friedhelm SchwenkerResearch on object classification based on tactile perception data is much less than that based on visual image data
In response to the above problems, we propose a target classification model based on pure tactile perception data
We proposed an effective target classification model (ResNet10-v1) based on pure tactile perception data
Summary
Friedhelm SchwenkerResearch on object classification based on tactile perception data is much less than that based on visual image data. Tactile sensor technology and the continuous development of deep-learning processes promote interdisciplinary research robot target recognition [5,6]. Tactile sensor technology has rapidly developed, and there have been many advances in performance and applications [7,8,9]. The authors based it on the k-nearest neighbor classifier, which uses dynamic time warp to calculate the distance between different time series that can successfully identify the target.
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