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All issues Volume 354 (2026) EPJ Web Conf., 354 (2026) 03001 References
Open Access
Issue
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
Article Number 03001
Number of page(s) 16
Section Robotics, Autonomous Systems, and Smart Inspection
DOI https://doi.org/10.1051/epjconf/202635403001
Published online 02 March 2026
  1. A. Gharbi, A dynamic reward-enhanced Q-learning approach for efficient path planning and obstacle avoidance in mobile robotics. Appl. Comput. Inform. 2024, in press. https://doi.org/10.1108/ACI-10-2023-0089 [Google Scholar]
  2. Q. Dong, P. Zeng, G. Wan, Y. He, X. Dong, Kalman filter-based one-shot sim-to-real transfer learning. IEEE Robot. Autom. Lett. 9, 311–318 (2024). [Google Scholar]
  3. Q. Zhou, Y. Lian, J. Wu, M. Zhu, H. Wang, J. Cao, An optimized Q-learning algorithm for mobile robot local path planning. Knowl.-Based Syst. 286, 111400 (2024). [Google Scholar]
  4. H. Ju, R. Juan, R. Gomez, K. Nakamura, G. Li, Transferring policy of deep reinforcement learning from simulation to reality for robotics. Nat. Mach. Intell. 4, 1077–1087 (2022). [Google Scholar]
  5. E. S. Low, P. Ong, C. Y. Low, An empirical evaluation of Q-learning in autonomous mobile robots in static and dynamic environments using simulation. Decis. Anal. J. 8, 100314 (2023). [Google Scholar]
  6. Ç. Kaymak, A. Uçar, C. Güzeliş, Development of a new robust stable walking algorithm for a humanoid robot using deep reinforcement learning with multi-sensor data fusion. Electronics 12, 568 (2023). [Google Scholar]
  7. A. A. N. Kumaar, S. Kochuvila, Mobile service robot path planning using deep reinforcement learning. IEEE Access 11, 100083–100096 (2023). [Google Scholar]
  8. A. A. N. Kumaar, S. Kochuvila, Reinforcement learning based path planning using a topological map for mobile service robot. Proc. IEEE Int. Conf. Electron. Comput. Commun. Technol. 1, 1–6 (2023). [Google Scholar]
  9. Y. Wang, Y. Fang, P. Lou, J. Yan, N. Liu, Deep reinforcement learning based path planning for mobile robot in unknown environment. J. Phys. Conf. Ser. 1576, 012009 (2020). [Google Scholar]
  10. K. R. Kunduru, Y. D. Dwivedi, R. Aruna, G. R. Thippeswamy, S. Selvakumar, M. Sudhakar, Elevating performance for enhancing AI-powered humanoid robots through innovation. In: Applied AI and Humanoid Robotics for the Ultra-Smart Cyberspace; IGI Global, pp. 85–119 (2023). [Google Scholar]
  11. N. Chukwurah, A. S. Adebayo, O. O. Ajayi, Sim-to-real transfer in robotics: Addressing the gap between simulation and real-world performance. JFMR 5, 33–39 (2024). [Google Scholar]
  12. N. N. V. S. Prakash, V. S. Reddy, V. Chandran, J. Amudha, Autonomous driving mobile robot using Q-learning. Proc. Int. Conf. Futurist. Technol. 1, 1–8 (2022). [Google Scholar]
  13. R. Tiwari, A. Srinivaas, R. K. Velamati, Adaptive navigation in collaborative robots: A reinforcement learning and sensor fusion approach. Appl. Syst. Innov. 8, 9 (2025). [Google Scholar]
  14. T. Ma, J. Lyu, J. Yang, R. Xi, Y. Li, J. An, C. Li, CLSQL: Improved Q-learning algorithm based on continuous local search policy for mobile robot path planning. Sensors 22, 5910 (2022). [Google Scholar]
  15. L. Dong, Z. He, C. Song, X. Yuan, H. Zhang, Multi-robot social-aware cooperative planning in pedestrian environments using attention-based actor-critic. Artif. Intell. Rev. 57, 108 (2024). [Google Scholar]
  16. P. Chandrakanth, M.S. Anbarasi, Privacy Preserving in Data Stream Mining Using Statistical Learning Methods for Building Ensemble Classifier. In: Smart Intelligent Computing and Applications, pp. 631–638. Springer Singapore, Singapore (2018). [Google Scholar]
  17. P. Michael Joseph Stalin, P. N. Rao, M. Palaniappan, P. M. Kumar, K. Udhayakumar, Effective thermal performance assessment for prismatic triangular solar air heater integrated with fins and turbulators. Numer. Heat Transf. Part A Appl. 86, 8129–8145 (2025). [Google Scholar]
  18. M. Balaji, S. N. Dinesh, S. V. Vetrivel, P. M. Kumar, R. Subbiah, Augmenting agility in production flow through ANP. Mater. Today Proc. 47, 5308–5312 (2021). [Google Scholar]
  19. C. Balakrishna, D.S. Mani, A.B. Reddy, P. Chandrakanth, R.S. Selvan, Two-Stage Deep Learning-YouTube Video Recommendation Process. In: 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS), vol. 1, pp. 1–7. IEEE (2023). [Google Scholar]
  20. N. Senthil Kannan, R. Parameshwaran, P. T. Saravanakumar, P. M. Kumar, M. L. Rinawa, Performance and quality improvement in a foundry industry using fuzzy MCDM and lean methods. Arab. J. Sci. Eng. 47, 15379–15390 (2022). [Google Scholar]
  21. T. Ravichandra, P. Murugeswari, M. Revathi, S. Senthilkumar, D. S. Rathore, M. Sudhakar, Future of machine learning and robotics in digital technology for hospitality. In: Cutting-Edge Technologies for Business Sectors; IGI Global, pp. 401–428 (2023). [Google Scholar]
  22. C. Lavanpriya, V. Muthukumaran, P. Manoj Kumar, Evaluating suppliers using AHP in a fuzzy environment and allocating order quantities to each supplier in a supply chain. Math. Probl. Eng. 869598 (2022). [Google Scholar]
  23. T. R. Saravanan, S. Suvitha, D. Banavath, S. Gogula, J. Upadhyay, M. Sudhakar, AI and machine learning integration in medical assistive robotics. In: Fostering Cross-Industry Sustainability with Intelligent Technologies; IGI Global, pp. 130–151 (2023). [Google Scholar]

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