Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

An assembly system based on industrial robot with binocular stereo vision

Authors
Hong Tang, Nanfeng Xiao
Corresponding Author
Hong Tang
Available Online November 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.1How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 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  - https://doi.org/10.2991/aest-16.2016.1
ID  - Tang2016/11
ER  -