Grasp Pose Estimation for Pick and Place of Frozen Blood Bags Based on Point Cloud Processing and Deep Learning Strategies Using Vacuum Grippers

Abstract

We describe different strategies to compute grasp poses for vacuum grippers for pick and place of frozen blood bags. Our methods process RGB-D data to search for local flat patches on the bags’ surface which act as grasp points when using vacuum grippers. We develop three strategies which analyze point cloud data to propose gripper poses and one method that trains a real-time object detector to propose the grasp point and processes point cloud data to compute the bag’s orientation. All the strategies are based on the computation of a normal vector at each 3D point to account for the surface orientation. They differ from each other based on how each method searches for these flat patches. We validate and compare the effectiveness of our methods by conducting real-world pick and place experiments, achieving an average success rate of above 80%. In conclusion, four different strategies, both analytical and a hybrid of analytic and deep learning approaches to infer optimal grasp poses for vacuum grippers to automatise pick and place operations of blood bags were presented.