Design of a Teat Cup Attachment Robot for Automatic Milking Systems

Chengjun Wang, Fan Ding (), Liuyi Ling and Shaoqiang Li
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Chengjun Wang: School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China
Fan Ding: School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China
Liuyi Ling: School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China
Shaoqiang Li: School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

Agriculture, 2023, vol. 13, issue 6, 1-22

Abstract: Automatic milking systems (AMSs) for medium and large dairy farms in China require manual assistance to attach the teat cup, which greatly affects the milking efficiency and labor costs. In this regard, it is necessary to realize the automatic completion of cow teat attachment work. To address this issue, the authors developed a teat cup attachment robot for an AMS based on the theory of the solution of inventive problems (TRIZ). Specifically, we developed an enhanced algorithm for teat detection and designed a six-degree-of-freedom manipulator with integrated drive control. The design parameters were simulated and analyzed to validate their efficacy, while the rationality of the manipulator’s movement during teat cup attachment was verified. The maximum displacement and angle error of the cup was 1.625 mm and 1.216 mm, respectively, as verified by the teat cup attachment error test. A dynamic response test showed that the manipulator could follow the teat of the cow in real time. The attachment time for teat cups was 21 s per cow, with a success rate of 98%. The performance of the teat cup attachment robot was capable of meeting the automatic attachment teat cup needs for medium and large dairy farms during milking.

Keywords: teat cup attachment robot; AMS; deep learning; TRIZ; simulation design (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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