Detection method for the cucumber robotic grasping pose in clutter scenarios via instance segmentation

The application of robotic grasping for agricultural products pushes automation in agriculture-related industries. Cucumber, a common vegetable in greenhouses and supermarkets, often needs to be grasped from a cluttered scene. In order to realize efficient grasping in cluttered scenes, a fully automatic cucumber recognition, grasping, and palletizing robot system was constructed in this paper. The system adopted Yolact++ deep learning network to segment cucumber instances. An early fusion method of F-RGBD was proposed, which increases the algorithm's discriminative ability for these appearance-similar cucumbers at different depths, and at different occlusion degrees. The results of the comparative experiment of the F-RGBD dataset and the common RGB dataset on Yolact++ prove the positive effect of the F-RGBD fusion method. Its segmentation masks have higher quality, are more continuous, and are less false positive for prioritizing-grasping prediction. Based on the segmentation result, a 4D grab line prediction method was proposed for cucumber grasping. And the cucumber detection experiment in cluttered scenarios is carried out in the real world. The success rate is 93.67% and the average sorting time is 9.87 s. The effectiveness of the cucumber segmentation and grasping pose acquisition method is verified by experiments.