점군 기반의 심층학습을 이용한 파지 알고리즘
Grasping Algorithm using Point Cloud-based Deep Learning
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (10)
- L. P. Ellekilde, "Applying a learning framework for improving success rates in industrial bin picking", 2012IEEE, 2012, 1637~1643
- J. Mahler, "Learning deep policies for robot bin picking by simulating robust grasping sequences", Machine Learning Research, 78, pp.515~524 (2017)
- "ShapeNet : An information-rich 3d model repository" (2015)[URL]
- Y. Jiang, "Efficient grasping from rgbd images : Learning using a new rectangle representation", 2011 IEEE International Conference on Robotics and Automation, pp.3304~3311 (2011)
- I. Lenz, "Deep learning for detecting robotic grasps", The International Journal of Robotics Research (2015)[DOI]
- R. B. Rusu, "3d is here : Point cloud library(pcl)", 2011 IEEE International Conference on Robotics and Automation (2011)
- R. B. Rusu, "Fast point feature histograms(FPFH)for 3D registration", 2009 IEEE International Conference on Robotics and Automation, pp.3212~3217 (2009)
- S. Rusinkiewicz, "Efficient variants of the ICP algorithm", Third International Conference on 3-D Digital Imaging and Modeling, pp.145~152 (2001)
- K. He, "Deep residual learning for image recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770~778 (2016)
- C. R. Qi, "PointNet: Deep learning on point sets for 3d classification and segmentation", 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.652~660 (2017)