SYSTEMS TO INTELLIGENT AUTOMATION
Keywords:
Machine Learning Algorithms, Industry Problem-Solving, Smart Robots, Industrial Automation, Robotic Process SystemsAbstract
This study explores the profound impact of artificial intelligence on
industrial automation and focuses on the revolution of autonomous systems. A detailed
literature study shows major improvements in AI technologies such as machine
learning, computer vision, and natural language processing that improve the
capabilities of robotic systems. Key approaches used in diverse studies include
integrating AI with legacy systems, making real-time decisions, and developing
intelligent robotic systems. Data quality issues, integration with current infrastructures,
and the challenges of human-robot collaboration are all rigorously examined, as are the
ethical considerations and security risks that come with the introduction of AI-driven
technology. The findings emphasize the importance of addressing operational
challenges in order to fully benefit from AI-enabled automation, paving the way for a
more sustainable and efficient industrial landscape.
References
1.
Al Shahrani, A. M., Alomar, M. A., Alqahtani, K. N., Basingab, M. S., Sharma,
B., and Rizwan, A., (2023) Machine learning-enabled smart industrial automation
systems using internet of things, Sensors, vol. 23, no. 1, p. 324.
2.
Dhameliya, N., (2023) Revolutionizing PLC systems with AI: a new era of
industrial automation, American Digits: Journal of Computing and Digital
Technologies.
3.
Donepudi, P., (2018) Application of artificial intelligence in automation
industry, Asian Journal of Applied Science and Engineering, vol. 7, no. 1,.
4.
Elnadi, M. and Abdallah, Y. O., (2024) Industry 4.0: critical investigations and
synthesis of key findings, Management Review Quarterly, vol. 74, pp. 711-744,.
https://doi.org/10.1007/s11301-022-00314-4.
5.
Gevarter, W. B., (1984) An overview of artificial intelligence and robotics, U.S.
Department of Bureau of Standards, January.
