MultipleCupSuctionNet: Deep Neural Network for Detecting Grasp Pose of a Vacuum Gripper with Multiple Suction Cups Based on YOLO Feature Map Affine Transformation

Multiple-suction-cup grasp is preferable for pick- ing large and heavy objects in warehouses. Deep learning methods have been widely used to predict grasp point position for single-suction-cup grasp, but few studies have examined grasp pose detection for a gripper with multiple suction cups. This study proposes MultipleCupSuctionNet, which is a deep neural network for detecting multiple cup grasp pose. To address the challenge of direct regression of poses, this neural network first infers the surface mask to compute the surface normal to obtain the direction of the gripper z-axis. The feature maps of the surfaces are then affine-transformed to surface image coordinates, based on which gripper position and rotation angle around the z-axis are predicted. Such a neural network design makes grasp pose learning easier because there is no need to consider the orientation of the surfaces so that 2D poses respective to the surface are learned. Feature map affine transformation saves computation cost because there is no need to first transform images and then extract the features to obtain surface features. Further, for each predicted grasp pose, the overlap area between cup and surface is calculated to determine which cup should be used when grasping. MultipleCupSuctionNet exhibited competitive performance (80.1% prediction accuracy) particularly in dense scenes compared with state-of-the-art planners (Dex-Net and a model-free multiple-suction-cup grasp planner). Physical picking experiments were conducted using a robot employing the proposed neural network. The experimental results showed that our robot achieved an average success rate of 94.5% for picking common objects in warehouses.