Influence of Surface Tactile Data Quantity on Material Classification in Unstructured Environments
Dongyan Nie, Jialin Liu, Xiaoying Sun
Abstract
The tactile sensor design and data measurements have been playing an important role in surface material recognition. In inevitable unstructured environments, the performance of the material classification is bottlenecked by multimodal deficiency or data collection intermittency. This article investigates the influence of surface tactile data quantity on material classification in unstructured environments. We extracted tactile data features according to the material-surface-texture model and proposed an approach to determine the scope of the time window of tactile data. We utilized the machine learning classifiers to verify the effectiveness of the hand-designed features and the determined time window. Based on the combination of the Mel frequency cepstrum coefficient and statistics, the majority of the algorithms can classify the tactile data of the upper limit (60 ms in this article) with an accuracy of at least 85%. Exploiting tactile data of the lower limit (50 ms in this article), linear discriminant analysis, quadratic discriminant analysis, and support vector machine can achieve about 93% classification accuracy, and area under curve is about 0.99. The accuracy is not significantly improved with the time window beyond the upper limit, while the performance is degraded and unstable with it beneath the lower limit.