_TechRxiv_Grasp_Detection.pdf (2.62 MB)

Deep Learning-based Fast Grasp Planning for Robotic Bin-picking by Small Data Set without GPU

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posted on 2021-04-11, 13:24 authored by SHOGO ARAISHOGO ARAI, ZHUANG FENG, Fuyuki TokudaFuyuki Tokuda, Adam Purnomo, Kazuhiro Kosuge
This paper proposes a deep learning-based fast grasp detection method with a small dataset for robotic bin-picking. We consider the problem of grasping stacked up mechanical parts on a planar workspace using a parallel gripper. In this paper, we use a deep neural network to solve the problem with a single depth image. To reduce the computation time, we propose an edge-based algorithm to generate potential grasps. Then, a convolutional neural network (CNN) is applied to evaluate the robustness of all potential grasps for bin-picking. Finally, the proposed method ranks them and the object is grasped by using the grasp with the highest score. In bin-picking experiments, we evaluate the proposed method with a 7-DOF manipulator using textureless mechanical parts with complex shapes. The success ratio of grasping is 97%, and the average computation time of CNN inference is less than 0.23[s] on a laptop PC without a GPU array. In addition, we also confirm that the proposed method can be applied to unseen objects which are not included in the training dataset.


Email Address of Submitting Author

Submitting Author's Institution

Tohoku University

Submitting Author's Country

  • Japan

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