Pick and Place Robot Using Visual Feedback Control and Transfer Learning-Based CNN

Fusaomi Nagata, Kohei Miki, Akimasa Otsuka, Kazushi Yoshida, Keigo Watanabe, Maki K. Habib

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Artificial neural network (ANN) which has four or more layers structure is called deep NN (DNN) and it is recognized as one of promising machine learning techniques. Convolutional neural network (CNN) is widely used and powerful structure for image recognition and/or defect inspection. It is also known that support vector machine (SVM) has a superior ability for binary classification in spite of only having two layers. The authors already have developed a CNN SVM design and training tool for defect detection of resin molded articles, while the effectiveness and the validity have been proved through several CNNs design, training and evaluation. The tool further enables to facilitate the design of a CNN model based on transfer learning concept. In this paper, a pick and place robot is introduced while implementing a visual feedback control and a transfer learning-based CNN. The visual feedback control enables to omit the complicated calibration between image and robot coordinate systems, also the transfer learning-based CNN allows the robot to estimate the orientation of target objects for dexterous picking operation. The usefulness and validity of the system is confirmed through pick and place experiments using a small articulated robot named DOBOT.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Mechatronics and Automation, ICMA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages850-855
Number of pages6
ISBN (Electronic)9781728164151
DOIs
Publication statusPublished - Oct 13 2020
Event17th IEEE International Conference on Mechatronics and Automation, ICMA 2020 - Beijing, China
Duration: Oct 13 2020Oct 16 2020

Publication series

Name2020 IEEE International Conference on Mechatronics and Automation, ICMA 2020

Conference

Conference17th IEEE International Conference on Mechatronics and Automation, ICMA 2020
Country/TerritoryChina
CityBeijing
Period10/13/2010/16/20

Keywords

  • convolutional neural network
  • pick and place
  • robot
  • transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization

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