REFERENCES
1. Eirale, A.; Martini, M.; Chiaberge, M. Human following and guidance by autonomous mobile robots: a comprehensive review. IEEE. Access. 2025, 13, 42214-53.
2. Du, J.; Vann, W.; Zhou, T.; Ye, Y.; Zhu, Q. Sensory manipulation as a countermeasure to robot teleoperation delays: system and evidence. Sci. Rep. 2024, 14, 4333.
3. Vallaro, M. F. Markerless motion capture for dual-handed teleportation of industrial robots. Master’s thesis, University of Rhode Island, 2024. https://digitalcommons.uri.edu/theses/2565/. (accessed 2026-03-19).
4. Gongor, F.; Tutsoy, O. On the remarkable advancement of assistive robotics in human-robot interaction-based health-care applications: an exploratory overview of the literature. Int. J. Hum. Comput. Interact. 2024, 41, 1502-42.
5. Fan, A.; Wang, Y.; Hu, Z.; Yang, L.; Fan, X.; Yang, Z. Multi-dimensional performance verification of ship fuel consumption prediction model under dynamic operating conditions. Energy 2025, 332, 137120.
6. Abdullah, A.; Blow, D.; Chen, R.; Uthai, T.; Du, E. J.; Islam, M. J. Human-machine interfaces for subsea telerobotics: from soda-straw to natural language interactions. arXiv 2024, arXiv:2412.01753. Available online: https://doi.org/10.48550/arXiv.2412.01753. (accessed 19 Mar 2026).
7. Farhadi, M. H.; Rabiee, A.; Ghafoori, S.; Cetera, A.; Xu, W.; Abiri, R. Human-centred shared autonomy for motor planning, learning, and control applications. arXiv 2025, arXiv:2506.16044. Available online: https://doi.org/10.48550/arXiv.2506.16044. (accessed 19 Mar 2026).
8. Stolcis, F. Predictive algorithm and guidance for human motion in robotics teleoperation. Ph.D. Dissertation, Politecnico di Torino, 2024. https://webthesis.biblio.polito.it/id/eprint/30813. (accessed 2026-03-19).
9. Long, Y.; Pan, L. Human motion recognition information based on LSTM recurrent neural network algorithm. In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China. Sep 27-29, 2024; IEEE; 2024. pp. 819-25.
10. Sathya, D.; Saravanan, G.; Thangamani, R. Fuzzy logic and its applications in mechatronic control systems. In Computational intelligent techniques in mechatronics; Wiley, 2024; pp. 211-41.
11. Duorinaah, F. X.; Rajendran, M.; Kim, T. W.; et al. Human and multi-robot collaboration in indoor environments: a review of methods and application potential for indoor construction sites. Buildings 2025, 15, 2794.
12. Jia, J.; Zhou, H.; Zhang, X. An agile large-workspace teleoperation interface based on human arm motion and force estimation. arXiv 2024, arXiv:2410,15414. Available online: https://arxiv.org/html/2410.15414v1. (accessed 19 Mar 2026).
13. Zou, R.; Liu, Y.; Li, Y.; Chu, G.; Zhao, J.; Cai, H. A novel human intention prediction approach based on fuzzy rules through wearable sensing in human-robot handover. Biomimetics 2023, 8, 358.
14. Lu, C.; Jin, L.; Liu, Y.; Wang, J.; Li, W. Teleoperated grasping using data gloves based on fuzzy logic controller. Biomimetics 2024, 9, 116.
15. Ren, X.; Li, Z.; Zhou, M.; Hu, Y. Human intention-aware motion planning and adaptive fuzzy control for a collaborative robot with flexible joints. IEEE. Trans. Fuzzy. Syst. 2023, 31, 2375-88.
16. Liu, A.; Chen, T.; Zhu, H.; Fu, M.; Xu, J. Fuzzy variable impedance-based adaptive neural network control in physical human–robot interaction. Proc. Inst. Mech. Eng. Part. I. J. Syst. Control. Eng. 2022, 237, 220-30.
17. Li, Y.; Ge, S. S. Human–robot collaboration based on motion intention estimation. IEEE/ASME. Trans. Mechatron. 2014, 19, 1007-14.
18. Kizilkaya, B.; She, C.; Zhao, G.; Ali Imran, M. Intelligent mode-switching framework for teleoperation. In 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, May 13-17, 2024; IEEE, 2024; pp. 15692-8.
19. Torielli, D.; Franco, L.; Pozzi, M.; et al. Wearable haptics for a marionette-inspired teleoperation of highly redundant robotic systems. arXiv 2025, arXiv:2503.15998. Available online: https://doi.org/10.48550/arXiv.2503.15998. (accessed 19 Mar 2026).
20. Mohammed, M. N.; Aljibori, H. S. S.; Jameel Al-Tamimi, A. N.; et al. A new approach to design and development of in-pipe inspection robots. In 2024 Arab ICT Conference (AICTC), Manama, Bahrain, Feb 27-28, 2024; IEEE, 2024; pp. 108-12.
21. Al-Ansarry, S.; Al-Darraji, S.; Shareef, A.; Honi, D. G.; Fallucchi, F. Bi-directional adaptive probabilistic method with a triangular segmented interpolation for robot path planning in complex dynamic-environments. IEEE. Access. 2023, 11, 87747-59.
22. Rahmani-Fard, J.; Ridha, A. M.; Chyad, M. H.; Mohammed, M. J. Optimization of wind power system with fuzzy-PI control for axial flux-switching PM generator. In 2025 12th Iranian Conference on Renewable Energies and Distributed Generation (ICREDG), Qom, Iran, Feb 26-26, 2025; IEEE, 2025; p. 1-4.
23. Algburi, R. N. A.; Sultan, Aljibori. H. S. S.; Al-Huda, Z.; Sowoud, K. M.; Algabri, R.; Al-Antari, M. A. HHLP-SSA: enhanced fault diagnosis in industrial robots using hierarchical hyper-laplacian prior and singular spectrum analysis. In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkiye, Sep 21-22, 2024; IEEE, 2024; p. 1-8.
24. Abd Al-Sahb, W. S.; Alkhafaji, A. A. A.; Jweeg, M. J.; et al. The systematic comparison between the traditional and fuzzy control charts based on the medium and range with a practical application. In Hamdan, A.; Harraf, A.; Eds.; Business development via AI and digitalization. Cham: Springer Nature Switzerland, 2024; pp. 797-823.
25. Xiao, J.; Wang, B.; Huang, K.; Terzi, S.; Wang, W.; Macchi, M. Intelligent disassembly scenario understanding for human behavior and intention recognition towards self-perception human-robot collaboration system. J. Manuf. Syst. 2025, 83, 937-62.
26. Algburi, R. N. A.; Aljibori, H. S. S.; Al-Huda, Z.; Gu, Y. H.; Al-Antari, M. A. Advanced fault diagnosis in industrial robots through hierarchical hyper-laplacian priors and singular spectrum analysis. Complex. Intell. Syst. 2025, 11, 282.
27. Almngoshi, H. Z.; Mithra, C.; Kanaka Ratnam, A. S.; Shanmugam, S.; Saravanan, V.; Marapelli, B. Data visualisation models for analytics use artificial intelligence to predict diabetes in women. J. Mach. Comput. 2025, 5, 551-60.
28. Abdul Raheem, A. K.; Zuhair, M. Enhancing credit risk management in the banking sector through machine learning-based predictive models. J Theor Appl Inform Technol 2023, 101, 16. https://www.researchgate.net/publication/373923311_ENHANCING_CREDIT_RISK_MANAGEMENT_IN_THE_BANKING_SECTOR_THROUGH_MACHINE_LEARNING-BASED_PREDICTIVE_MODELS. (accessed 2026-03-19).
29. Algburi, R. N. A.; Aljibori, H. S. S.; Al-Huda, Z.; Sowoud, K. M.; Algabri, R.; Al-Antari, M. A. SSA-sparse MHD: singular spectrum analysis paired with sparse maximum harmonics deconvolution for detecting feeble defect signals in industrial robots. In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkiye, Sep 21-22, 2024; IEEE, 2024. p. 1-9.
30. Mohammed, M. N.; Aljibori, H. S. S.; Al-Tamimi, A.; et al. Toward Sustainable smart cities in Bahrain: a groundbreaking approach to marine renewable energy harnessing ssea tides and waves for a greener energy future. In 2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Bahrain, Bahrain, Oct 25-27, 2023; IEEE, 2023; p. 1-5.
32. Goodfellow, I.; Bengio, Y.; Courville, A. Deep learning. MIT Press, 2016. https://www.deeplearningbook.org/. (accessed 2026-03-19).
33. Khalil, H. K. Nonlinear systems, 3rd ed.; Prentice Hall, 2002. https://testbankdeal.com/sample/nonlinear-systems-3rd-edition-khalil-solutions-manual.pdf. (accessed 2026-03-19).
34. Slotine, J. J. E.; Li, W. Applied nonlinear control. Prentice Hall, 1991. https://www.academia.edu/download/33582713/Applied_Nonlinear_Control_Slotine.pdf. (accessed 2026-03-19).
35. Cohen, J. Statistical power analysis for the behavioral sciences, 2nd ed.; Lawrence Erlbaum Associates, 1988.
36. Butterworth, S. On the theory of filter amplifiers. Wirel. Eng. 1930, 7, 536-41. https://www.changpuak.ch/electronics/downloads/On_the_Theory_of_Filter_Amplifiers.pdf. (accessed 2026-03-19).
