New scientific article in Smart Agricultural Technology on Deep Learning during grasping

During the past week, a new scientific article has been published, emphasising novel results from the RoBUTCHER project. The paper, entitled “Deep Learning Model for Automatic Limb Detection and Gripping in a Novel Meat Factory Cell” is led by the RoBUTCHER team at Ciklum (Ukraine), in collaboration with researchers at NMBU.

The goal of the work was development of a deep-learning model which may be used in conjunction with the RoBUTCHER “Meat Factory Cell” platform, namely to enable an industrial robot within the system to identify and successfully grip the limbs of an entire pig carcass. The model consists of three main components: (1) a U-Net-based deep learning model that predicts heatmaps with a probability distribution of gripping and key point locations on the limbs, within RGB-D images; (2) a post-processing element for the extraction of keypoints from heatmaps, and transferral of these points into 3D space using a pinhole camera model; and (3) gripper orientation estimation, which uses the predicted limb key points to define gripper orientation in 3D space. The proposed system demonstrates high precision and robustness in estimating gripping points on pig limbs based on a data test set, which includes two gripping definitions: Norwegian and Danish. These gripping definitions account for variation in the slaughter process in two different European countries.

The full paper is available via Open Access here: https://www.sciencedirect.com/science/article/pii/S2772375524000911