The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Grasping Algorithm using Point Cloud-based Deep Learning

점군 기반의 심층학습을 이용한 파지 알고리즘

  • Received : 2020.11.23
  • Accepted : 2021.03.02
  • Published : 2021.05.31
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Abstract

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

Keywords

Acknowledgement

This work was supported by IITP grant funded by the Korea Government MSIT. (No. 2018-0-00622)

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