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Ant Colony Optimization Algorithm for Industrial Robot Programming in a Digital Twin

Bansal, Ridhi; Ahmadieh Khanesar, Mojtaba; Branson, David

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Authors

Ridhi Bansal

Mojtaba Ahmadieh Khanesar

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DAVID BRANSON DAVID.BRANSON@NOTTINGHAM.AC.UK
Professor of Dynamics and Control



Abstract

Advanced manufacturing that is adaptable to constantly changing product designs often requires dynamic changes on the factory floor to enable manufacture. The integration of robotic manufacture with machine learning approaches offers the possibility to enable such dynamic changes on the factory floor. While ensuring safety and the possibility of losses of components and waste of material are against their usage. Furthermore, developments in design of virtual environments makes it possible to perform simulations in a virtual environment, to enable human-in-the-loop production of parts correctly the first time like never before. Such powerful simulation and control software provides the means to design a digital twin of manufacturing environment in which trials are completed at almost at no cost. In this paper, ant colony optimization is used to program an industrial robot to avoid obstacles and find its way to pick and place objects during an assembly task in an environment containing obstacles that must be avoided. The optimization is completed in a digital twin environment first and movements transferred to the real robot after human inspection. It is shown that the proposed methodology can find the optimal solution, in addition to avoiding collisions, for an assembly task with minimum human intervention.

Citation

Bansal, R., Ahmadieh Khanesar, M., & Branson, D. (2019). Ant Colony Optimization Algorithm for Industrial Robot Programming in a Digital Twin. In Proceedings of the 25th International Conference on Automation & Computing, Lancaster University, Lancaster UK, 5-7 September 2019 (1-5). https://doi.org/10.23919/IConAC.2019.8895095

Conference Name 2019 25th International Conference on Automation and Computing (ICAC)
Conference Location Lancaster, UK
Start Date Sep 5, 2019
End Date Sep 7, 2019
Acceptance Date Jun 21, 2019
Online Publication Date Nov 11, 2019
Publication Date 2019-09
Deposit Date Jul 18, 2019
Publicly Available Date Mar 29, 2024
Publisher Institute of Electrical and Electronics Engineers
Pages 1-5
Book Title Proceedings of the 25th International Conference on Automation & Computing, Lancaster University, Lancaster UK, 5-7 September 2019
ISBN 978-1-7281-2518-3
DOI https://doi.org/10.23919/IConAC.2019.8895095
Keywords manufacturing; artificial intelligence; programming robot; digital twin; ant colony optimization,
Public URL https://nottingham-repository.worktribe.com/output/2319025
Publisher URL https://ieeexplore.ieee.org/document/8895095
Related Public URLs http://www.cacsuk.co.uk/index.php/conferences/icac

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