A Reconfigurable Industrial Robot Architecture for Smart Manufacturing
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DOI:
https://doi.org/10.67228/30715725/IJIARE-2019PI4V7NPublished 01-04-2019
Reconfigurable Robotics, Smart Manufacturing, Industry 4.0, Modular Robots, Cyber–Physical Systems, Intelligent Automation, Digital Twins, Flexible Production Issue
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ArticlesHow to Cite
[1]R. K. Sharma, “A Reconfigurable Industrial Robot Architecture for Smart Manufacturing”, IJIARE, vol. 2, no. 1, pp. 01–15, Jan. 2019, doi: 10.67228/30715725/IJIARE-2019PI4V7N.Abstract
Smart manufacturing in the era of Industry 4.0 requires flexible, intelligent, and adaptable robotic systems to handle dynamic production environments. Traditional industrial robots are typically single-purpose and lack the flexibility needed for mass customization and rapid product changes. To address this limitation, this paper proposes a Reconfigurable Industrial Robot Architecture (RIRA) designed to provide modularity, scalability, intelligence, and interoperability. The architecture integrates mechanical, electrical, and software modularity within a cyber-physical system where robotic modules function as plug-and-play components that can be dynamically reconfigured without interrupting production. RIRA consists of four layers: the physical modular layer, control and communication layer, intelligence and decision layer, and enterprise integration layer. These layers interact using digital twins, distributed control strategies, and standard communication protocols. The system incorporates Artificial Intelligence, Machine Learning, and Industrial Internet of Things to enable autonomy, predictive maintenance, and fault tolerance. A key feature is the self-reconfiguration mechanism that allows robots to automatically adapt their structure and tools based on production requirements through real-time optimization and reinforcement learning. Simulation results show that RIRA can reduce setup time by up to 60% and improve production line productivity by over 35%, demonstrating its potential as a foundation for future smart factories.
References
[1] Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., & Chirikjian, G. S. (2007). Modular Self-Reconfigurable Robot Systems: Challenges and Opportunities for the Future. IEEE Robotics & Automation Magazine, 14(1), 43–52.
[2] Fukuda, T., & Nakagawa, Y. (1988). Dynamically Reconfigurable Robotic System. Proceedings of the IEEE International Conference on Robotics and Automation.
[3] Shen, W.-M., Will, P., Galstyan, A., & Cao, X. (2006). Hormone-Based Distributed Control for Modular Robots. Autonomous Robots, 20(2), 165–183.
[4] Koren, Y., Shpitalni, M. (2010). Design of Reconfigurable Manufacturing Systems. Journal of Manufacturing Systems, 29(4), 130–141.
[5] Mehrabi, M. G., Ulsoy, A. G., & Koren, Y. (2000). Reconfigurable Manufacturing Systems: Key to Future Manufacturing. Journal of Intelligent Manufacturing, 11(4), 403–419.
[6] Pons, J. L. (2005). Wearable Robots: Biomechatronic Exoskeletons. Wiley.
[7] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
[8] Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement Learning in Robotics: A Survey. The International Journal of Robotics Research, 32(11), 1238–1274.
[9] Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. (2018). Learning Hand-Eye Coordination for Robotic Grasping with Deep Reinforcement Learning. The International Journal of Robotics Research, 37(4-5), 421–436.
[10] Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press.
[11] Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
[12] Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In F. Tao & A. Y. C. Nee (Eds.), Integrated Intelligence in Industry 4.0 (pp. 85–113). Springer.
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How to Cite
[1]R. K. Sharma, “A Reconfigurable Industrial Robot Architecture for Smart Manufacturing”, IJIARE, vol. 2, no. 1, pp. 01–15, Jan. 2019, doi: 10.67228/30715725/IJIARE-2019PI4V7N.
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