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Adaptive Industrial Robots Using Machine Vision

[+] Author Affiliations
Vladimir Kuts, Tauno Otto, Toivo Tähemaa, Khuldoon Bukhari, Tengiz Pataraia

Tallinn University of Technology, Tallinn, Estonia

Paper No. IMECE2018-86720, pp. V002T02A093; 8 pages
  • ASME 2018 International Mechanical Engineering Congress and Exposition
  • Volume 2: Advanced Manufacturing
  • Pittsburgh, Pennsylvania, USA, November 9–15, 2018
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5201-9
  • Copyright © 2018 by ASME


The use of industrial robots in modern manufacturing scenarios is a rising trend in the engineering industry. Currently, industrial robots are able to perform pre-programmed tasks very efficiently irrespective of time and complexity. However, often robots encounter unknown scenarios and to solve those, they need to cooperate with humans, leading to unnecessary downtime of the machine and the need for human intervention. The main aim of this study is to propose a method to develop adaptive industrial robots using Machine Learning (ML)/Machine Vision (MV) tools. The proposed method aims to reduce the effort of re-programming and enable self-learning in industrial robots. The elaborated online programming method can lead to fully automated industrial robotic cells in accordance with the human-robot collaboration standard and provide multiple usage options of this approach in the manufacturing industry. Machine Vision (MV) tools used for online programming allow industrial robots to make autonomous decisions during sorting or assembling operations based on the color and/or shape of the test object. The test setup consisted of an industrial robot cell, cameras and LIDAR connected to MATLAB through a Robot Operation System (ROS). The online programming tests and simulations were performed using Virtual/Augmented Reality (VR/AR) toolkits together with a Digital Twin (DT) concept, to test the industrial robot program on a digital object before executing it on the real object, thus creating a safe and secure test environment.

Copyright © 2018 by ASME
Topics: Machinery , Robots



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