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

Fusaomi Nagata, Maki K. Habib, Keigo Watanabe

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)142-150
Number of pages9
JournalInternational Journal of Mechatronics and Automation
Volume8
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • CNN
  • Convolutional neural network
  • Pick and place
  • Robot
  • Transfer learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Mechanics
  • Industrial and Manufacturing Engineering
  • Computational Mathematics
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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