Abstract: Regular feed pushing is an important link in the feeding process of dairy cows, and the intelligent feeding assistance of robot instead of human is becoming the development direction in the future. The feeding robot can complete multiple feeding throughout the day, which is widely used in the feeding of dairy cows in large and medium-sized pastures. However, the existing feeding robot has a single function, which can only complete the uniform feeding function, and can not meet the personalized feeding needs of cattle. To solve this problem, this paper proposes an intelligent pushing method of feeding assistant robot. The QR code label is introduced as the positioning label of cow neck rail, and the detection frame area of QR code label and cow head is obtained based on YOLOv4 deep learning model. The detection frame area of QR code label is recognized and tracked in real time through preprocessing and prediction algorithm, and the QR code label and cow head are matched to determine the position of feeding neck rail; According to the position matching information of cow-code and the residual forage distribution information, the robot push plate was controlled to change the pushing angle to realize personalized pushing and meet the individual feeding needs of dairy cows. Research and test results show that the proposed intelligent push method has a recognition rate of 96% for the QR code; In the case of losing 60 consecutive frames, the tracking and prediction accuracy of the QR code is less than ± 2.85%; The processing time of each frame in GPU is 34.4 ms; The accuracy of intelligent feeding is 100%, which can meet the real-time requirements of intelligent pushing in complex environment.

Key words: robot, intelligent pushing, feeding cows, QR code, deep learning