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Decentralised vs partially centralised self-organisation model for mobile robots in large structure assembly

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journal contribution
posted on 20.09.2018, 12:25 by Spartak Ljasenko, Pedro FerreiraPedro Ferreira, Laura JusthamLaura Justham, Niels LohseNiels Lohse
Currently, manufacturing companies are heavily investing into the automation of manufacturing processes. The push to improve productivity and efficiency is increasing the demand for more flexible and adaptable solutions than the currently common dedicated automation systems. In this paper, the planning problem for mobile robots in large structure assembly was addressed. Despite near-optimal results, the previously developed hybrid agent behaviour model was found to lack responsiveness and scalability. For that reason, an alternative, fully decentralised agent behaviour model was developed and compared to the hybrid one. Through simulated experiments, it was found that the decentralised agent behaviour model achieved much higher responsiveness; however, it required additional spare capacity to compensate for its decision-making imperfections.

Funding

The authors acknowledge support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation, in undertaking this research work under grant reference number EP/IO33467/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Computers in Industry

Citation

LJASENKO, S. ... et al., 2018. Decentralised vs partially centralised self-organisation model for mobile robots in large structure assembly. Computers in Industry, 104, pp.141-154.

Publisher

Elsevier © The Authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

07/09/2018

Publication date

2018-09-25

Notes

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International licence (CC BY). Full details of this licence are available at https://creativecommons.org/licenses/by/4.0/

ISSN

0166-3615

Language

en