Research on robot obstacle avoidance algorithm based on convolutional neural network

Traditional obstacle avoidance algorithms usually use a single shallow application, such as sensor-based distance measurement or some logic judgment algorithm, which leads to problems such as the need to manually adjust the parameters first, the inability to recognize complex or unknown environments, and the recognition errors caused by significant noise errors. Therefore, to overcome these limitations, this paper combines convolutional neural network and obstacle avoidance algorithms. A model of obstacle avoidance method based on convolutional neural network established in this paper, and puts forward the theory of obstacle avoidance method based on convolutional neural network, which adopts MobileNet_v3 as the learning framework, roughly classifies all the obstacle maps into three categories, and then, through the research and application of six traditional obstacle avoidance algorithms, finally concludes that the model can be applied according to different kinds of obstacles. The model can learn and discriminate against different obstacle maps, thus improving the performance of obstacle avoidance and avoiding the limitations of traditional obstacle avoidance algorithms. Verified the effectiveness of each algorithm in various scenarios. A single shallow application of the problem is usually used to robotize the traditional obstacle avoidance algorithms, which provides an essential reference.