Implementation and evaluation of calibration-less visual feedback controller for a robot manipulator DOBOT on a sliding rail

Fusaomi Nagata, Kei Furuta, Kohei Miki, Maki K. Habib, Keigo Watanabe

Research output: Contribution to journalArticlepeer-review

Abstract

Image processing and computer vision systems are adequate to enhance the potential functions of industrial robots. However, besides complex implementation, it is required to do time-consuming and complex calibrations between the robots and cameras before running it. This paper first introduces the design of a compact CNN to estimate objects' orientation. Then, the paper presents a desktop-sized pick and place robot while implementing a pixel-based visual feedback (PBVF) controller to move to target objects autonomously. The response of the VF control is quantitatively evaluated by counting control actions until the end-effector reaches the object position. A sliding rail is further considered to enable the robot to work in a broader working space. The PBVF controller is extended to utilise the sliding rail by dealing with the direction of the sliding rail as the robot's y-axis. Finally, the implementation of the PBVF controller and the effectiveness of the robot system with the sliding rail are presented through pick and place experiments of objects randomly placed on a table.

Original languageEnglish
Pages (from-to)142-150
Number of pages9
JournalInternational Journal of Mechatronics and Automation
Volume9
Issue number3
DOIs
Publication statusPublished - 2022

Keywords

  • CNN
  • convolutional neural network
  • desktop-sized robot
  • pick and place
  • pixel-based visual feedback control
  • sliding rail

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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