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 ...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.展开更多
基金supported by the Beijing Innovation Consortium of Agriculture Research System (BAIC12).
文摘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.
