Robot Perceptual Classification Method Based on Mixed Features of Decision Tree and Random Forest

Abstract

Under the background of the booming development of big data and the robot industry, the existing navigation technology has a certain delay in the identification of robot drive form and the unstructured surface physical environment. In this paper, by processing the statistical data of the robot foot sensor, the hybrid features of the robot in different environments are constructed in the two dimensions of time domain and frequency domain (fast Fourier transform and continuous wavelet transform). Finally, by combining decision tree and random forest algorithm, the mixed features are fully learned, and the high-precision classification results are obtained.