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Propose a library robot autonomous obstacle avoidance control method based on improved machine learning, which controls the library robot to automatically avoid obstacles during movement and achieve ideal working results. By using various sets of ultrasonic distance sensors installed on the body of the library robot, the distance information between the library robot and the target obstacle is collected. The minimum distance information collected by each set of sensors is combined to form a perception environment feature vector, which is used as input for the convolutional neural network. After convolution, pooling and other operations, the perception results of the library robot on the current environment are output. At the same time, particle swarm optimization algorithm is used to improve the convolutional neural network parameters. Then, the optimal environment recognition result is combined with the distance between the library robot and the obstacle and the real-time speed of the library robot under this result as effective inputs for the fuzzy PID control model. After input and output variable fuzzification, fuzzy reasoning, and output variable deblurring operations, the library robot achieves autonomous obstacle avoidance and conflict free operation. The experimental results show that using this method for obstacle avoidance control, the autonomous obstacle avoidance control effect is good, and the response can be made at a long distance, the obstacle avoidance driving distance is short, and the response is faster when running at high speed; In a complex static environment, the robot can avoid multiple obstacles stably and smoothly and reach the finish line. Moreover, the recognition and classification results of this method are consistent with the actual perceived environment types, which provides a reliable guarantee for library robots to avoid obstacles autonomously.