JACIII Vol.24 No.7 pp. 953-962
doi: 10.20965/jaciii.2020.p0953


Visual Servoing with Deep Learning and Data Augmentation for Robotic Manipulation

Jingshu Liu and Yuan Li

Beijing Institute of Technology
5 South Zhongguancun Street, Haidian District, Beijing 100081, China

Corresponding author

October 19, 2020
November 17, 2020
December 20, 2020
visual servoing, deep learning, CNN, robotic manipulation, data augmentation

We propose a visual servoing (VS) approach with deep learning to perform precise, robust, and real-time six degrees of freedom (6DOF) control of robotic manipulation to ease the extraction of image features and estimate the nonlinear relationship between the two-dimensional image space and the three-dimensional Cartesian space in traditional VS tasks. Owing to the superior learning capabilities of convolutional neural networks (CNNs), autonomous learning to select and extract image features from images and fitting the nonlinear mapping is achieved. A method for designing and generating a dataset from few or one image, by simulating the motion of an eye-in-hand robotic system is described herein. Therefore, network training requiring a large amount of data and difficult data collection occurring in actual situations can be solved. A dataset is utilized to train our VS convolutional neural network. Subsequently, a two-stream network is designed and the corresponding control approach is presented. This method converges robustly with the experimental results, in that the position error is less than 3 mm and the rotation error is less than 2.5° on average.

Cite this article as:
J. Liu and Y. Li, “Visual Servoing with Deep Learning and Data Augmentation for Robotic Manipulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.7, pp. 953-962, 2020.
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