DU Xuedan1,2, CAI Yinghao1, LU Tao1, WANG Shuo1, YAN Zhe2
1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
2. School of Automation, Harbin University of Science and Technology, Harbin 150080, China
Abstract:A method is proposed to detect the optimal position of robotic grasping based on deep neural network. Compared with conventional manually-set features, the features learned by deep neural network methods are more robust and stabler, and can be applied to objects outside of the training set. In this method, the object detection algorithm based on deep learning is first used to detect the objects in the image with the classes and locations of the objects recorded. Then, the robotic grasping method based on deep learning is used to learn the grasping positions according to the object classification and detection results. Simulation experiments indicate that the proposed method can classify the objects in the images accurately, and the grasping experimental results on Universal Robot 5 verify the effectiveness of the proposed method.
[1] Maitin-Shepard J, Cusumano-Towner M, Lei J, et al. Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2010:2308-2315.
[2] Ramisa A, Alenya G, Moreno-Noguer F, et al. Using depth and appearance features for informed robot grasping of highly wrinkled clothes[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2012:1703-1708.
[3] Jiang Y, Moseson S, Saxena A. Efficient grasping from RGBD images:Learning using a new rectangle representation[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2011:3304-3311.
[4] Lin Y, Sun Y. Robot grasp planning based on demonstrated grasp strategies[J]. International Journal of Robotics Research, 2015, 34(1):26-42.
[5] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[6] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[M]//Advances in Neural Information Processing Systems. Cambridge, USA:MIT Press, 2012:1097-1105.
[7] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[8] Varley J, Weisz J, Weiss J, et al. Generating multi-fingered robotic grasps via deep learning[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2015:4415-4420.
[9] Johns E, Leutenegger S, Davison A J. Deep learning a grasp function for grasping under gripper pose uncertainty[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2016:4461-4468.
[10] Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps[J]. International Journal of Robotics Research, 2015, 34(4/5):705-724.
[11] Ren S Q, He K M, Girshick R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[M]//Advances in Neural Information Processing Systems. Cambridge, USA:MIT Press, 2015:91-99.
[12] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA:IEEE, 2014:580-587.
[13] Uijlings J R R, van de Sande K E A, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2):154-171.
[14] Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision. Piscataway, USA:IEEE, 2015:1440-1448.
[15] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[C]//13th European Conference on Computer Vision. Berlin, German:Springer, 2014:818-833.
[16] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[A/OL]. (2015-04-10)[2017- 04-18]. https://arxiv.org/abs/1409.1556v6.