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Title: 3D vision assisted flexible robotic assembly of machine components
Authors: Ogun, Philips S.
Usman, Zahid
Dharmaraj, Karthick
Jackson, Michael R.
Keywords: 3D vision
Automated assembly
Object recognition
Point cloud matching
Issue Date: 2015
Publisher: © SPIE
Citation: OGUN, P. ... et al., 2015. 3D vision assisted flexible robotic assembly of machine components. IN: Proceedings of 2015 8th International Conference on Machine Vision (ICMV 2015), Barcelona, Spain, 19-21 November 2015 (Proceedings of SPIE, 9875, DOI: 10.1117/12.2229053).
Abstract: Robotic assembly systems either make use of expensive fixtures to hold components in predefined locations, or the poses of the components are determined using various machine vision techniques. Vision-guided assembly robots can handle subtle variations in geometries and poses of parts. Therefore, they provide greater flexibility than the use of fixtures. However, the currently established vision-guided assembly systems use 2D vision, which is limited to three degrees of freedom. The work reported in this paper is focused on flexible automated assembly of clearance fit machine components using 3D vision. The recognition and the estimation of the poses of the components are achieved by matching their CAD models with the acquired point cloud data of the scene. Experimental results obtained from a robot demonstrating the assembly of a set of rings on a shaft show that the developed system is not only reliable and accurate, but also fast enough for industrial deployment.
Description: © 2015 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Version: Published
DOI: 10.1117/12.2229053
URI: https://dspace.lboro.ac.uk/2134/25454
Publisher Link: http://dx.doi.org/10.1117/12.2229053
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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