Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence
ISSN: 0144-5154
Article publication date: 12 November 2019
Issue publication date: 3 December 2020
Abstract
Purpose
The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques.
Design/methodology/approach
This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined.
Findings
This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable.
Originality/value
Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.
Keywords
Acknowledgements
This project was supported by the Royal Academy of Engineering under the Research Chairs and Senior Research Fellowships scheme. Professor Ashutosh Tiwari is Airbus/RAEng Research Chair in Digitisation for Manufacturing at the University of Sheffield. The authors would also like to acknowledge the Manufacturing Technology Centre for their support.
Citation
Oyekan, J., Fischer, A., Hutabarat, W., Turner, C. and Tiwari, A. (2020), "Utilising low cost RGB-D cameras to track the real time progress of a manual assembly sequence", Assembly Automation, Vol. 40 No. 6, pp. 925-939. https://doi.org/10.1108/AA-06-2018-078
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited