Deep Convolution and Gated Recurrent Unit Network for Robot Perceptual Intelligent Recognition

Abstract

Intelligent prediction and pattern recognition (PR) classification technology based on deep learning has attracted widespread attention from scholars in the field because it does not rely on subjective human analysis and reasoning. In this paper, a variant of convolutional neural network and recurrent neural network (RNN) is constructed by combining feature engineering and deep learning. A convolution-gated recurrent unit network (i.e., CONV-GRU) is proposed by improving the network structure and optimization algorithm. The model has an accurate effect on the recognition of the contact surface of autonomous mobile robots, with an accuracy rate of 90%. In addition, CONV-GRU also solves the over-fitting phenomenon of the benchmark model (RNN, LSTM, GRU). The research aims to solve the key problems of intelligent robot in tactile sensing, signal processing and intelligent computing technology.