In order to control library robots to avoid obstacles automatically in the process of traveling and achieve an ideal work-ing effect,an autonomous obstacle avoidance control method of library robots based on improved machine learning is proposed.Collect the distance information between the library robot and the target obstacle,perceive the environment feature vector taken as the input of convolutional neural network,and output the perception results of library robots in the current environment after the convolution and pooling.The results are processed through operations such as the input and output variable fuzzification,fuzzy reasoning,and output variable defuzzification,thus implementing autonomous obstacle avoidance and non conflict operation of library robots.Experi-mental results show that this method has the advantages of good autonomous obstacle avoidance control effect,short obstacle avoid-ance driving distance,and faster response when running at high speed.Meanwhile,it can avoid multiple obstacles,and the recogni-tion and classification results are consistent with the actual perceived environmental types.
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