ScholarWorks@성균관대학교

초록

The robotic pick-and-place system is applied widely in many fields such as assembly, packaging, bin-picking, and sorting. In this paper, we present a deep learning and analytical-based method for generating antipodal grasping multi-modality in highly cluttered scenes. Our method takes advantage of three types of grasp poses to deal with the complexity of environment and achieves efficient computation time for real applications. A new synthetic training datasets are generated in Isaac Sim including approximately 35000 RGB-D images and an automatic labeling algorithm is developed. We utilize convolutional neural networks (CNNs) for predicting antipodal grasping parameters on objects and a filtering algorithm to avoid collisions and calculate grasp's depth simultaneously. Our approach processes the entire task in approximately 0.2 seconds, achieving a success rate of over 96% and more than 98% collision-free grasps in cluttered scenes. The method was verified by experiments with RB10 robot arm, two-fingers grippers, depth camera L515, and several objects in different scenes. The article shows a simple, effective, and highly applicable approach in real environments. The real experimental video of our method is shown at https://www.youtube.com/watch?v=GvJZxUyQr3w.

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