An assembly system based on industrial robot with binocular stereo vision

Hong Tang; Nanfeng Xiao

doi:10.1117/12.2265855

23 January 2017 An assembly system based on industrial robot with binocular stereo vision

Hong Tang, Nanfeng Xiao

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 1032247 (2017) https://doi.org/10.1117/12.2265855
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

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.

Citation Download Citation

Hong Tang and Nanfeng Xiao "An assembly system based on industrial robot with binocular stereo vision", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 1032247 (23 January 2017); https://doi.org/10.1117/12.2265855

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available