Robotic 3D Vision-Guided System for Half-Sheep Cutting Robot

Shuai Mu, Haibo Qin, Jia Wei, Qingkang Wen, Sihan Liu, Shucai Wang and Shengyong Xu

Mathematical Problems in Engineering, 2020, vol. 2020, 1-11

Abstract:

Sheep body segmentation robot can improve production hygiene, product quality, and cutting accuracy, which is a huge change for traditional manual segmentation. With reference to the New Zealand sheep body segmentation specification, a vision system for Cartesian coordinate robot cutting half-sheep was developed and tested. The workflow of the vision system was designed and the image acquisition device with an Azure Kinect sensor was developed. Furthermore, a LabVIEW software with the image processing algorithm was then integrated with the RGBD image acquisition device in order to construct an automatic vision system. Based on Deeplab v3+ networks, an image processing system for locating ribs and spine was employed. Taking advantage of the location characteristics of ribs and spine in the split half-sheep, a calculation method of cutting line based on the key points is designed to determine five cutting curves. The seven key points are located by convex points of ribs and spine and the root of hind leg. Using the conversion relation between depth image and the space coordinates, the 3D coordinates of the curves were computed. Finally, the kinematics equation of the rectangular coordinate robot arm is established, and the 3D coordinates of the curves are converted into the corresponding motion parameters of the robot arm. The experimental results indicated that the automatic vision system had a success rate of 98.4% in the cutting curves location, 4.2 s time consumption per half-sheep, and approximately 1.3 mm location error. The positioning accuracy and speed of the vision system can meet the requirements of the sheep cutting production line. The vision system shows that there is potential to automate even the most challenging processing operations currently carried out manually by human operators.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1520686

DOI: 10.1155/2020/1520686

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