A comparative study of time-related performance: COBOT with industrial robots using digital twin robotic cells

In the context of the industry 4.0, defined by highly personalized processes, there’s a rising interest of technologies related to the extraction of data of the process in real time to better decide future actions i.e. define more efficient and productive configurations of the industrial plant. From this, Cyber-physical systems such as digital twins are the most suited technology to follow the process performance through data and reliably validate different outcomes from simulations. Furthermore, COBOTs that can adapt to the environment are ideal to deal with such changing demands which have to work alongside industrial robots. Therefore, this study aims to use the digital twin capabilities of data extraction and simulation from the FANUC’s robot simulator software to validate the performance of industrial and collaborative robots in order to offer insights to better apply each type of robot. It was used a robotic cell with the robot PRBT 6 as a reference to make 2 digital twins of the cell with 2 different robot models, one collaborative and one industrial. Two digital twins were made using ROBOGUIDE simulator, version 9.4 for the COBOT and version 9.1 for the industrial robot. It’s presented the method used to build the digital twin of the robotic cell and the results show that the cycle time of the COBOT’s cell is significantly higher than the industrial one but has higher flexibility.

Authors

Publication Details

Journal
International Robotics & Automation Journal
Published
2024-11-08
DOI
https://doi.org/10.15406/iratj.2024.10.00290
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
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article

A comparative study of time-related performance: COBOT with industrial robots using digital twin robotic cells

Paulo Santos, Rogério Adas Pereira Vitalli, Julio Cesar de Almeida Freitas, Frederico Garavati Fioretti et al.
International Robotics & Automation Journal
Modular Robots and Swarm Intelligence
article

A comparative study of time-related performance: COBOT with industrial robots using digital twin robotic cells

Paulo Santos, Rogério Adas Pereira Vitalli, Julio Cesar de Almeida Freitas, Frederico Garavati Fioretti, Marcelo Campos Rebouças
article en

Abstract

In the context of the industry 4.0, defined by highly personalized processes, there’s a rising interest of technologies related to the extraction of data of the process in real time to better decide future actions i.e. define more efficient and productive configurations of the industrial plant. From this, Cyber-physical systems such as digital twins are the most suited technology to follow the process performance through data and reliably validate different outcomes from simulations. Furthermore, COBOTs that can adapt to the environment are ideal to deal with such changing demands which have to work alongside industrial robots. Therefore, this study aims to use the digital twin capabilities of data extraction and simulation from the FANUC’s robot simulator software to validate the performance of industrial and collaborative robots in order to offer insights to better apply each type of robot. It was used a robotic cell with the robot PRBT 6 as a reference to make 2 digital twins of the cell with 2 different robot models, one collaborative and one industrial. Two digital twins were made using ROBOGUIDE simulator, version 9.4 for the COBOT and version 9.1 for the industrial robot. It’s presented the method used to build the digital twin of the robotic cell and the results show that the cycle time of the COBOT’s cell is significantly higher than the industrial one but has higher flexibility.

International Robotics & Automation JournalVol. 10(3)
Openalex Percentile: Top 79%
Modular Robots and Swarm Intelligence
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A comparative study of time-related performance: COBOT with industrial robots using digital twin robotic cells — Paulo Santos, Rogério Adas Pereira Vitalli, et al. · International Robotics & Automation Journal (2024) | TGRS Research Map | TGRS