Modelling uncertainties in human-robot industrial collaborations

Wang, Xiaodan, Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 and Mohammed, Abdullah 2022. Modelling uncertainties in human-robot industrial collaborations. Procedia Computer Science 207 , pp. 3652-3661. 10.1016/j.procs.2022.09.425

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Abstract

With the rise of Industry 4.0 technological trends, there is a growing tendency in manufacturing automation towards collaborative robots. Human-robot collaboration (HRC) is motivated by the combination of complementary human and robot skills and intelligence, which can increase productivity, flexibility and adaptability. However, it is still challenging to achieve safe and efficient human-robot collaborative systems due to the dynamics of human presence, uncertainties in the dynamic environment, and the need for adaptability. Such uncertainties could relate to the human-robot capabilities and availability, parts positioning, unexpected obstacles, etc. This paper develops time-based simulations and event-based simulations to model and analyse the dynamic factors in human-robot collaboration systems. The novelty of this work is the systematic modelling and analysis of dynamic factors in HRC manufacturing scenarios through the development of digital simulations of human-robot collaboration scenarios while considering the dynamic nature of humans and environments. A real-world industrial case study was redesigned into a collaborative workstation. The simulated scenario is developed using the software called Tecnomatix Process Simulate, which can help to visualise the dynamic factors and analyse the impact of the factors on the HRC. The simulation illustrates and analyses possible uncertainties in human-robot industrial collaborative workstations, which can contribute to the future design of HRC industrial workstations and the optimisation of productivity.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1877-0509
Funders:	WEFO
Date of First Compliant Deposit:	20 October 2022
Date of Acceptance:	1 August 2022
Last Modified:	08 Jul 2023 17:26
URI:	https://orca.cardiff.ac.uk/id/eprint/153603

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