Abstract: Combined with the working principle of the robot and the application of convolutional neural network (CNN) of image classification, a wall obstacle detection and recognition algorithm based on CNN is proposed. Firstly, with the goal of accurate recognitionof wall obstacles, the image database of wall obstacles is constructed, and then the simplified VGG-16 network is optimized to obtain a CNN model, which is suitable for accurate recognition of wall obstacles. On this basis, the network is designed to be composed of an input layer, four convolutional layers, two pooling layers, a fully-connected layer and an output layer. Further, the training samples are convolved using 3×3 convolution kernels, and the acquired feature maps are pooled in 2×2 domains. After repeating the above operations, the optimal network model is obtained by learning and the network model parameters are further determined. The experimental results show that the recognition accuracy of obstacles can reach 99.0%, which has good recognition ability.

Key words: cleaning robot, obstacle recognition, convolutional neural network, deep learning