Title

Transfer learning-based and originally-designed CNNs for robotic pick and place operation

Second Author's Department

Mechanical Engineering Department

Find in your Library

https://doi.org/10.1504/IJMA.2021.118430

Document Type

Research Article

Publication Title

International Journal of Mechatronics and Automation

Publication Date

1-1-2021

doi

10.1504/IJMA.2021.118430

Abstract

The authors have developed a CNN and SVM design and training application for defect detection, and the effectiveness and the usefulness have been proved through several design, training and classification experiments. In this paper, the application further enables to facilitate the design of transfer learning-based CNNs. After introducing the application, a pick and place robot system based on DOBOT is proposed while implementing a visual feedback controller and a transfer learning-based CNN. The visual feedback controller is applied to avoiding the complicated calibration task between image and robot coordinate systems, also the transfer learning-based CNN allows to detect the orientation of target objects for dexterous picking operation. The effectiveness of the proposed system is demonstrated through pick and place tests using gripper type and suction cup type tools. Finally, an originally designed CNN with shallower layers is compared with the AlexNet's transfer learning-based CNN in terms of classification scores.

First Page

142

Last Page

150

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