Robot Ground Classification and Recognition Based on CNN-LSTM Model

Abstract

In order to improve the accuracy of mobile robot ground classification, a robot ground recognition method based on convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed. The method takes historical collected data as input, builds a CNN architecture composed of one-dimensional convolutional layer and pooling layer, and extracts high-dimensional features reflecting the complex dynamic changes of the robot's walking state. The CNN architecture constructs the proposed feature vector into a time series form as the input of the LSTM network and models the dynamic changes of the learning features. Taking the public data set in the CareerCon 2019 competition as experimental data, the results show that the algorithm in this paper has achieved better classification results on 9 types of ground. Comparative experiments with other advanced methods show that this method can further improve the classification accuracy of the robot on the ground, and provide technical support for realizing more effective ground environment perception and navigation control operation in the best state.