International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Review of Artificial Intelligence, Machine Learning, and Deep Learning in Advanced Robotics

Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have significantly transformed the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments. These technologies enable key capabilities such as autonomous navigation, object recognition and manipulation, natural language processing, and predictive maintenance. AI, ML, and DL are also instrumental in developing collaborative robots (cobots) that can work alongside humans and adjust to dynamic environments and tasks.

Beyond robotics, these technologies play a vital role in enhancing transportation systems by improving safety, efficiency, and passenger convenience. In manufacturing, they enable assembly robots to operate with greater precision, safety, and intelligence. Additionally, AI-driven advancements in aviation management help optimize operations, reduce costs, and enhance customer satisfaction. In the taxi industry, these technologies contribute to safer, more efficient, and customer-friendly services.

This research provides an overview of the current developments and diverse applications of AI, ML, and DL in advanced robotics, highlighting their impact across multiple sectors. It also identifies areas for further study to bridge the gaps in existing research, aiming to improve robotic performance and drive productivity in the robotics industry.

Country : India

1 Manoj Kumar K M

  1. India

IRJIET, Volume 9, Issue 5, May 2025 pp. 434-441

doi.org/10.47001/IRJIET/2025.905048

Full Paper
Download

References

  1. M. Woschank, E. Rauch, and H. Zsifkovits, “A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics,” Sustainability, vol. 12, p. 3760, 2020.
  2. S. Fahle, C. Prinz, and B. Kuhlenkötter, “Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application,” Procedia CIRP, vol. 93, pp. 413–418, 2020.
  3. D. Vrontis et al., “Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review,” Int. J. Human Res. Manag., vol. 33, pp. 1237–1266, 2022.
  4. J. Howard, “Artificial intelligence: implications for the future of work,” Am. J. Ind. Med., vol. 62, pp. 917–926, 2019.
  5. J.F. Arinez, Q. Chang, R.X. Gao, C. Xu, and J. Zhang, “Artificial intelligence in advanced manufacturing: current status and future outlook,” J. Manuf. Sci. Eng., vol. 142, 2020.
  6. I.Andras et al., “Artificial intelligence and robotics: a combination that is changing the operating room,” World J. Urol., vol. 38, pp. 2359–2366, 2020.
  7. Q. Bai et al., “Object detection recognition and robot grasping based on machine learning: a survey,” IEEE Access, vol. 8, pp. 181855–181879, 2020.
  8. W.J. Lee et al., “Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data,” Procedia CIRP, vol. 80, pp. 506–511, 2019.
  9. X. Jiang et al., “A survey on artificial intelligence in Chinese sign language recognition,” Arabian J. Sci. Eng., vol. 45, pp. 9859–9894, 2020.
  10. S. Panesar et al., “Artificial intelligence and the future of surgical robotics,” Ann. Surg., vol. 270, pp. 223–226, 2019.
  11. T. Le Nguyen and T.T.H. Do, “Artificial intelligence in healthcare: a new technology benefit for both patients and doctors,” in 2019 Portland Int. Conf. on Management of Engineering and Technology (PICMET), IEEE, pp. 1–15, 2019.
  12. S. Sarker et al., “Robotics and artificial intelligence in healthcare during COVID-19 pandemic: a systematic review,” Robot. Auton. Syst., vol. 146, p. 103902, 2021.
  13. S. He, L.G. Leanse, and Y. Feng, “Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases,” Adv. Drug Deliv. Rev., vol. 178, p. 113922, 2021.
  14. S. Wasilow and J.B. Thorpe, “Artificial intelligence, robotics, ethics, and the military: a Canadian perspective,” AI Magazine, vol. 40, pp. 37–48, 2019.
  15. M.T. Linaza et al., “Data-driven artificial intelligence applications for sustainable precision agriculture,” Agronomy, vol. 11, p. 1227, 2021.
  16. R. Belk, “Ethical issues in service robotics and artificial intelligence,” Serv. Ind. J., vol. 41, pp. 860–876, 2021.
  17. B.B. Elallid et al., “A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving,” J. King Saud Univ. Comput. Inf. Sci., 2022.
  18. H. Ning, R. Yin, A. Ullah, and F. Shi, “A survey on hybrid human-artificial intelligence for autonomous driving,” IEEE Trans. Intell. Transp. Syst., vol. 23, pp. 6011–6026, 2021.
  19. M. El-Shamouty et al., “Simulation-driven machine learning for robotics and automation,” tm–TechnischesMessen, vol. 86, pp. 673–684, 2019.
  20. C. Lammie and M.R. Azghadi, “Memtorch: a simulation framework for deep memristive cross-bar architectures,” in Proc. 2020 IEEE Int. Symp. Circuits and Systems (ISCAS), pp. 1–5, 2020.
  21. P. Cooke, “Gigafactory logistics in space and time: Tesla’s fourth gigafactory and its rivals,” Sustainability, vol. 12, p. 2044, 2020.
  22. M. Soori, B. Arezoo, and M. Habibi, “Accuracy analysis of tool deflection error modelling in prediction of milled surfaces by a virtual machining system,” Int. J. Comput. Appl. Technol., vol. 55, pp. 308–321, 2017.
  23. M. Soori, B. Arezoo, and M. Habibi, “Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines,” J. Manuf. Syst., vol. 33, pp. 498–507, 2014.
  24. M. Soori, B. Arezoo, and M. Habibi, “Dimensional and geometrical errors of three-axis CNC milling machines in a virtual machining system,” Comput. Aided Des., vol. 45, pp. 1306–1313, 2013.
  25. M. Soori, B. Arezoo, and M. Habibi, “Tool deflection error of three-axis computer numerical control milling machines, monitoring and minimizing by a virtual machining system,” J. Manuf. Sci. Eng., vol. 138, 2016.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies