An assembly system based on industrial robot with binocular stereo vision

DOI

10.2991/aest-16.2016.1How to use a DOI?

Keywords

assembly system; industrial robot; binocular stereo vision; genetic algorithm; deep neural network.

Abstract

This paper proposes an electronic part and component assembly system based on an industrial robot with binocular stereo vision. Firstly, binocular stereo vision with a visual attention mechanism model is used to get quickly the image regions which contain the electronic parts and components. Secondly, a deep neural network is adopted to recognize the features of the electronic parts and components. Thirdly, in order to control the end-effector of the industrial robot to grasp the electronic parts and components, a genetic algorithm (GA) is proposed to compute the transition matrix and the inverse kinematics of the industrial robot (end-effector), which plays a key role in bridging the binocular stereo vision and the industrial robot. Finally, the proposed assembly system is tested in LED component assembly experiments, and the results denote that it has high efficiency and good applicability.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hong Tang
AU  - Nanfeng Xiao
PY  - 2016/11
DA  - 2016/11
TI  - An assembly system based on industrial robot with binocular stereo vision
BT  - Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 1
EP  - 9
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.1
DO  - 10.2991/aest-16.2016.1
ID  - Tang2016/11
ER  -