Simulation of An Automatic System of Robotics for Artificial Animated Being Manufacturing Using AnyLogic Simulation Software

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 5
Year of Publication : 2024
Authors : Murad Bashabsheh
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How to Cite?

Murad Bashabsheh, "Simulation of An Automatic System of Robotics for Artificial Animated Being Manufacturing Using AnyLogic Simulation Software," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 5, pp. 129-137, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I5P112

Abstract:

In this model, a Robot is employed to place raw artificial animated beings into three different tanks according to a specific sequence. Each artificial animated being must remain in each tank for a designated period, falling within a specified minimum and maximum duration. Should the maximum time limit be surpassed, the artificial animated being is deemed overcooked and discarded. Conversely, upon completing the cycle through the third tank, the artificial animated being transforms into a living being and departs. The robot, which can be considered as a crane, is designed as an agent comprising four statecharts: three manage the degrees of freedom, while one serves as the overarching controller. It features two types of interfaces: an Application Programming Interface (API) offering functions such as moveTo(), stop(), etc., and flowchart objects named UseRobot, applicable within Enterprise Library flowcharts, as demonstrated in this model.

Keywords:

Artificial Intelligence, Robotics, Automatic system, Simulation model, Any Logic. 

References:

[1] Iqbal H. Sarker, “AI-Based Modeling: Techniques, Applications and Research Issues towards Automation, Intelligent and Smart Systems,” SN Computer Science, vol. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Pei Wang, “On Defining Artificial Intelligence,” Journal of Artificial General Intelligence, vol. 10, no. 2, pp. 1-37, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Lijia Chen, Pingping Chen, and Zhijian Lin, “Artificial Intelligence in Education: A Review,” IEEE Access, vol. 8, pp. 75264-75278, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Pei Wang, Kai Liu, and Quinn Dougherty, “Conceptions of Artificial Intelligence and Singularity,” Information, vol. 9, no. 4, pp. 1-15, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] K. Winkle, and A. Van Maris, “Social Influence and Deception in Socially Assistive Robotics,” ICRES 2019: International Conference on Robot, pp. 45-46, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] D. Acemoglu and P. Restrepo, “Artificial Intelligence, Automation and Work,” National Bureau of Economic Research, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Manav Raj, and Robert Seamans, “Primer on Artificial Intelligence and Robotics,” Journal of Organization Design, vol. 8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Robert Niewiadomski, and Dennis Anderson, The Rise of Artificial Intelligence: Its Impact on Labor Market and Beyond, IGI Global, pp. 29-49, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Michael Brady, “Artificial Intelligence and Robotics,” Artificial Intelligence, vol. 26, no. 1, pp. 79-121, 1985.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Luca Iocchi et al., “Development of Intelligent Service Robots,” Intelligenza Artificiale, vol. 7, no. 2, pp. 139-152, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Lorenzo Sciavicco, and Bruno Siciliano, Modelling and Control of Robot Manipulators, Springer London, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Weiyu Wang, and Keng Siau, “Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity,” Journal of Database Management, vol. 30, no. 1, pp. 61-79, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] George Chryssolouris, Kosmas Alexopoulos, and Zoi Arkouli, “Artificial Intelligence in Manufacturing Equipment, Automation, and Robots,” A Perspective on Artificial Intelligence in Manufacturing, pp. 41-78, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Isaac Kofi Nti et al., “Applications of Artificial Intelligence in Engineering and Manufacturing: A Systematic Review,” Journal of Intelligent Manufacturing, vol. 33, no. 6, pp. 1581-1601, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Md Tahmid Bin Touhid et al., “Building a Cloud-Based Digital Twin for Remote Monitoring and Control of a Robotic Assembly System,” The International Journal of Advanced Manufacturing Technology, vol. 129, no. 9-10, pp. 4045-4057, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Yuan Zhao et al., “A Framework for Development of Digital Twin Industrial Robot Production Lines Based on a Mechatronics Approach,” International Journal of Modeling, Simulation, and Scientific Computing, vol. 14, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Sahil Belsare, Emily Diaz Badilla, and Mohammad Dehghanimohammadabadi, “Reinforcement Learning with Discrete Event Simulation: The Premise, Reality, and Promise,” 2022 Winter Simulation Conference (WSC), Singapore, pp. 2724-2735, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Mohammed Farhan, Brett Göhre, and Edward Junprung, “Reinforcement Learning in Anylogic Simulation Models: A Guiding Example Using Pathmind,” 2020 Winter Simulation Conference (WSC), Orlando, USA, pp. 3212-3223, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Soontorn Phiphopsuthipaiboon, and Somjai Boonsiri, “Business Process Reengineering - Case Study on Computer Center Service,” 2016 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016), vol. 81, pp. 1-4, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Samuel Hall et al., “Creation of a Development Platform for Remote Maintenance Solutions in Fusion Power Plants,” IEEE Transactions on Plasma Science, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Marius Matulis, and Carlo Harvey, “A Robot Arm Digital Twin Utilising Reinforcement Learning,” Computers & Graphics, vol. 95, pp. 106-114, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] A. Kolekar, S. Shalgar, and I. Malawade, “Beyond Reality: A Study of Integrating Digital Twins,” Journal of Physics: Conference Series, vol. 2601, no. 1, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Wenhui Fan et al., “Multi-Agent Modeling and Simulation in the AI Age,” Tsinghua Science and Technology, vol. 26, no. 5, pp. 608-624, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Djamilia F. Skripnuk, Kseniia N. Kikkas, and Viktor I. Merkulov, “Forecasting of the Global Market of Software that Uses Artificial Intelligence Algorithms,” Digital Transformation on Manufacturing, Infrastructure & Service, pp. 707-721, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Mareike Dornhöfer et al., “Simulation of Smart Factory Processes Applying Multi-Agent-Systems-A Knowledge Management Perspective,” Journal of Manufacturing and Materials Processing, vol. 4, no. 3, pp. 1-22, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Baohui Dong et al., “Simulation Study on Layout of Separate Lock Station in Automated Container Terminal,” 2021 6th International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, pp. 1419-1424, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Muhammad Monjurul Karim, Cihan H. Dagli, and Ruwen Qin, “Modeling and Simulation of a Robotic Bridge Inspection System,” Procedia Computer Science, vol. 168, pp. 177-185, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ilesanmi Daniyan et al., “Design and Simulation of a Flexible Manufacturing System for Manufacturing Operations of Railcar Subassemblies,” Procedia Manufacturing, vol. 54, pp. 112-117, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Leon McGinnis, Shannon Buckley, and Ali V. Barenji, “Designing and Implementing Operational Controllers for a Robotic Tote Consolidation Cell Simulation,” 2021 Winter Simulation Conference (WSC), Phoenix, USA, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Yanfang Wang, Xiangquan Chang, and Yuntian Chang, “Modeling and Simulation of Passenger Flow System in Ticket Hall of Jinan West Railway Station Based on AnyLogic,” 2023 IEEE International Conference on Real-time Computing and Robotics (RCAR), Datong, China, pp. 510-515, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Ch.K. Volos, I.M. Kyprianidis, and I.N. Stouboulos, “Experimental Investigation on Coverage Performance of a Chaotic Autonomous Mobile Robot,” Robotics and Autonomous Systems, vol. 61, no. 12, pp. 1314-1322, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Khalil Aloui et al., “Development of an AGV System Using MBSE Method and Multi-Agents’ Technology,” Design and Modeling of Mechanical Systems - V, pp. 103-114, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Hugo Perier, Eloise Matheson, and Mario Di Castro, “Web Based User Interface Solution for Remote Robotic Control and Monitoring Autonomous System,” Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Xu Sun, Hao Yu, and Wei Deng Solvang, “Measuring the Effectiveness of AI-Enabled Chatbots in Customer Service Using AnyLogic Simulation,” Advanced Manufacturing and Automation XII, pp. 266-274, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Yuefei Gu et al., “Simulating a Production System as an Agent-Based Model: A Case Study of a Gear Reducer Factory,” 2021 3rd International Symposium on Robotics & Intelligent Manufacturing Technology (ISRIMT), Changzhou, China, pp. 198-201, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Yang Xu et al., “Simulation Analysis of Isolated Lane Layout in Automated Container Terminal Yard,” 2021 6th International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[37] A.A. Lyubchenko et al., “Discrete-Event Simulation of Operation and Maintenance of Telecommunication Equipment Using AnyLogicBased Multi-State Models,” Journal of Physics: Conference Series, vol. 1441, pp. 1-12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[38] V.M. Antonova, N.A. Grechishkina, and N.A. Kuznetsov, “Analysis of the Modeling Results for Passenger Traffic at an Underground Station Using AnyLogic,” Journal of Communications Technology and Electronics, vol. 65, no. 6, pp. 712-715, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Yang Liu, and Yunxue Song, “Research on Simulation and Optimization of Road Traffic Flow Based on Anylogic,” 2022 8th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2022), vol. 360, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Y.G. Karpov, “Simulation Modeling Systems,” Introduction to Modeling with AnyLogic 5, St. Petersburg, 2005.
[Google Scholar]