Depth Image-Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking.

ABSTRACT: Bin-picking of small parcels and other textureless planar-faced objects is a common task at warehouses. A general color image-based vision-guided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for textureless objects. Further, prior preparation of huge numbers of goal images is impractical at a warehouse. In this paper, we propose a novel depth image-based vision-guided robot bin-picking system for textureless planar-faced objects. Our method uses a deep convolutional neural network (DCNN) model that is trained on 15,000 annotated depth images synthetically generated in a physics simulator to directly predict grasp points without object segmentation. Unlike previous studies tha

SUBMITTER: Jiang P 

PROVIDER: S-EPMC7038393 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature