Experimental Study on Intelligent Pushing Method of Feeding Assistant Robot

In the feeding of dairy cows in medium and large-scale pastures, the existing pushing robot has a single function, which can only complete the uniform feeding function, and can not meet the personalized feeding needs of each cow. To solve this problem, this paper proposed an intelligent pushing method of feeding assistant robot. Firstly, the two-dimensional code label is introduced as the positioning label of cow neck rail, and the detection frame area of the two-dimensional code label and the cow's head is obtained based on the YOLOv4 deep learning model. Then, the detection frame area of the two-dimensional code label is identified and tracked in real time through the preprocessing and prediction algorithm, and the two-dimensional code label is matched with the cow head to determine the feeding neck rail position of the foraging cow.Finally, based on the cow-rail position mat-ching information and residual material distribution information, the robot push plate is controlled to change the push angle to realize personalized push, so as to meet the individual feeding needs of dairy cows. The experimental results show that the recognition rate of the proposed intelligent pushing method is 96.0%. When the two-dimensional code label loses 60 frames continuously, the tracking and prediction accuracy of the two-dimensional code is within ±2.85%; the processing time of each frame image in the graphics processor is 34.4 ms; the accuracy of intelligent pushing is 100%, which meets the real-time requirement of robot intelligent pushing in complex environment of cowshed. © 2022, Editorial Department, Journal of South China University of Technology. All right reserved.