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All issues Volume 344 (2025) EPJ Web Conf., 344 (2025) 01059 References
Open Access
Issue
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
Article Number 01059
Number of page(s) 8
Section AI-Integrated Physics, Technology, and Engineering
DOI https://doi.org/10.1051/epjconf/202534401059
Published online 22 December 2025
  1. M. V. de Souza Almeida, A. B. Alves, E. S. Carvalho, E. C. C. da Silva, B. D. R. de Mesquita, Educational Robotics as a Teaching Field and Technology Integration: Application of CAD, CAM and 3D printing in structural robot construction. IEEE World Conference on Engineering Education (EDUNINE). 1–5 (2020). https://doi.org/10.1109/EDUNINE48860.2020.914 9541 [Google Scholar]
  2. N. Al-Mouh et al., A professional development workshop on advanced computing technologies for high and middle school teachers. 15th International Conference on Information Technology Based Higher Education and Training (ITHET). 1–4 (2016). https://doi.org/10.1109/ITHET.2016.7760696 [Google Scholar]
  3. A. C. T. de Vasconcelos et al., Utilizing Educational Robotics for Learning About Sustainability Issues. Latin American Robotics Symposium (LARS), Brazilian Symposium on Robotics (SBR), and Workshop on Robotics in Education (WRE). 384–388 (2022). https://doi.org/10.1109/LARS/SBR/WRE56824.20 22.9995840 [Google Scholar]
  4. A. Chopra, J. K. Chandel, V. K. Panchal, S. Sinha, Understanding of cognitive robotics. International Conference on Computing for Sustainable Global Development (INDIACom). 317–320 (2014). https://doi.org/10.1109/IndiaCom.2014.6828151 [Google Scholar]
  5. J. Liu et al., Development And Application of Robotics Technologies in Future Intelligent Soldier Squad. 35th Chinese Control and Decision Conference (CCDC). 845–850 (2023). https://doi.org/10.1109/CCDC58219.2023.103266 81 [Google Scholar]
  6. D. Deeb, I. Merkuryev, V. Pakhomov, New Educational Technologies for Training Specialists in the Field of Mechatronics and Robotics. 7th International Conference on Information Technologies in Engineering Education (Inforino). 1–4 (2024). https://doi.org/10.1109/Inforino60363.2024.10552009 [Google Scholar]
  7. Z. J. Belmonte, C. Bandola, R. Decapia, E. C. Gonzaga, Do Technological Developments Reduce Unemployment in the Philippines?. 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS). 1–5 (2021). https://doi.org/10.1109/ITMS52826.2021.9615294 [Google Scholar]
  8. L. Y. Y. Lu, Y. L. Lan, J. S. Liu, A novel approach for exploring technological development trajectories. IEEE International Conference on Management of Innovation & Technology (ICMIT). 504–509 (2012). https://doi.org/10.1109/ICMIT.2012.6225857 [Google Scholar]
  9. A. Ivoilov, V. Trubin, V. Zhmud, L. Dimitrov, The Power Consumption Decreasing of the Two- Wheeled Balancing Robot. International Multi- Conference on Industrial Engineering and Modern Technologies (FarEastCon). 1–8 (2018). https://doi.org/10.1109/FarEastCon.2018.8602775 [Google Scholar]
  10. K. Mitobe, G. Capi, A global stability analysis of legged balance controllers under ZMP constraints. IEEE International Symposium on Robotic and Sensors Environments (ROSE). 1–6 (2023). https://doi.org/10.1109/ROSE60297.2023.1041074 8 [Google Scholar]
  11. M. A. Imtiaz et al., Control System Design, Analysis & Implementation of Two Wheeled Self Balancing Robot (TWSBR). IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 431–437 (2018). https://doi.org/10.1109/IEMCON.2018.8614858 [Google Scholar]
  12. P. Suebsaiprom, C. L. Lin, 2-DOF barycenter mechanism for stabilization of fish-robots. IEEE 8th Conference on Industrial Electronics and Applications (ICIEA). 1119–1122 (2013). https://doi.org/10.1109/ICIEA.2013.6566534 [Google Scholar]
  13. X. Wang et al., Teleoperation control for bimanual robots based on RBFNN and wave variable. 9th International Conference on Modelling, Identification and Control (ICMIC). 1–6 (2017). https://doi.org/10.1109/ICMIC.2017.8321519 [Google Scholar]
  14. G. Masengo et al., A Design of Lower Limb Rehabilitation Robot and its Control for Passive Training. 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). 152–157 (2020). https://doi.org/10.1109/CYBER50695.2020.92789 52 [Google Scholar]
  15. M. A. Arteaga-Peréz, J. Pliego-Jiménez, J. G. Romero, Experimental Results on the Robust and Adaptive Control of Robot Manipulators Without Velocity Measurements. IEEE Transactions on Control Systems Technology. 28, 6, 2770–2773 (2020). https://doi.org/10.1109/TCST.2019.2945915 [Google Scholar]
  16. C. S. Putra et al., Multilayer Control for Coordinating Three - Wheeled Omnidirectional Mobile Robots. 6th International Conference on Instrumentation, Control, and Automation (ICA). 182–187 (2019). https://doi.org/10.1109/ICA.2019.8916737 [Google Scholar]
  17. R. P. Saputra et al., Decomposed-Objective Model Predictive Control for Autonomous Casualty Extraction. IEEE Access. 9, 39656–39679 (2021). https://doi.org/10.1109/ACCESS.2021.3063782 [Google Scholar]
  18. M. Zarghami et al., Fast and precise positioning of wheeled Omni-directional robot with input delay using model-based predictive control. In: Proceedings of the 33rd Chinese Control Conference. 7800–7804 (2014). https://doi.org/10.1109/ChiCC.2014.6896302 [Google Scholar]
  19. Q. A. Al-Haija et al., Cryptosystem based on Arduino Microcontroller Useful for Small Scale Networks. Procedia Comput Sci. 34, 639–646 (2014). https://doi.org/10.1016/j.procs.2014.07.091 [Google Scholar]
  20. A. A. Sulayman et al., Arduino microcontroller based real-time monitoring of haemodialysis process for patients with kidney disease. e-Prime - Advances in Electrical Engineering, Electronics and Energy. 7, 100403 (2024). https://doi.org/10.1016/j.prime.2023.100403 [Google Scholar]
  21. P. Itterheimová, F. Foret, P. Kubáň, High-resolution Arduino-based data acquisition devices for microscale separation systems. Anal Chim Acta. 1153, 338294 (2021). https://doi.org/10.1016/j.aca.2021.338294 [Google Scholar]
  22. R. Cuervo et al., Low-cost and open-source neonatal incubator operated by an Arduino microcontroller. HardwareX. 15, e00457 (2023). https://doi.org/10.1016/j.ohx.2023.e00457 [Google Scholar]
  23. N. A. Arsyad et al., Breast milk volume using portable double pump microcontroller Arduino Nano. Enferm Clin. 30, 555–558 (2020). https://doi.org/10.1016/j.enfcli.2019.07.159 [Google Scholar]
  24. J. A. Marín-Marín, P. A. García-Tudela, P. Duo-Terrón, Computational thinking and programming with Arduino in education: A systematic review for secondary education. Heliyon. 10, 8, e29177 (2024). https://doi.org/10.1016/j.heliyon.2024.e29177 [Google Scholar]
  25. B. Zhao, B. Y. Wang, J. Cheng, D. Xu, Study on Hybrid PID Control of Multi-Terrain Mobile Robot Based on Moment Balance Mechanism. 2nd International Conference on Automation, Robotics and Computer Engineering (ICARCE). 1–6 (2023). https://doi.org/10.1109/ICARCE59252.2024.1049 2547 [Google Scholar]
  26. L. Zhang, X. Ren, Q. Guo, Balance Control of a Wheeled Hopping Robot. 39th Chinese Control Conference (CCC). 3801–3805 (2020). https://doi.org/10.23919/CCC50068.2020.9189592 [Google Scholar]
  27. Z. Chen, X. Ruan, Y. Li, Balancing control of a cubical robot balancing on its corner. IEEE 15th International Workshop on Advanced Motion Control (AMC). 631–636 (2018). https://doi.org/10.1109/AMC.2019.8371167 [Google Scholar]
  28. F. F. Rabbany, A. Qurthobi, A. Suhendi, Design of Self-Balancing Virtual Reality Robot Using PID Control Method and Complementary Filter. IEEE International Conference on Industry 40, Artificial Intelligence, and Communications Technology (IAICT). 15–19 (2021). https://doi.org/10.1109/IAICT52856.2021.9532576 [Google Scholar]
  29. T. A. Mai, T. S. Dang, D. N. Anisimov, E. Fedorova, Fuzzy-PID Controller for Two Wheels Balancing Robot Based on STM32 Microcontroller. International Conference on Engineering Technologies and Computer Science (EnT). 20–4 (2019). https://doi.org/10.1109/EnT.2019.00009 [Google Scholar]
  30. N. Hasanah, A. H. Alasiry, B. Sumantri, Two Wheels Line Following Balancing Robot Control using Fuzzy Logic and PID on Sloping Surface. International Electronics Symposium on Engineering Technology and Applications (IES- ETA). 210–215 (2018). https://doi.org/10.1109/ELECSYM.2018.8615483 [Google Scholar]
  31. P. Grelewicz et al., Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems. IEEE Trans Syst Man Cybern Syst. 53, 7, 4226–4238 (2023). https://doi.org/10.1109/TSMC.2023.3244714 [Google Scholar]
  32. H. Sira-Ramírez, E. W. Zurita-Bustamante, C. Huang, Equivalence Among Flat Filters, Dirty Derivative-Based PID Controllers, ADRC, and Integral Reconstructor-Based Sliding Mode Control. IEEE Transactions on Control Systems Technology. 28, 5, 1696–1710 (2020). https://doi.org/10.1109/TCST.2019.2919822 [Google Scholar]
  33. W. Zhang, H. Lv, Disturbance Observer-Based PID Control System Using DNA Strand Displacement and Its Application in Exponential Gate. IEEE Access. 11, 113160–113175 (2023). https://doi.org/10.1109/ACCESS.2023.3320956 [Google Scholar]
  34. S. Gao et al., Global Nested PID Control of Strict- Feedback Nonlinear Systems With Prescribed Output and Virtual Tracking Performance. IEEE Transactions on Circuits and Systems II: Express Briefs. 67, 2, 325–329 (2020). https://doi.org/10.1109/TCSII.2019.2907141 [Google Scholar]
  35. W. Tan, W. Han, J. Xu, State-Space PID: A Missing Link Between Classical and Modern Control. IEEE Access. 10, 116540–116553 (2022). https://doi.org/10.1109/ACCESS.2022.3218657 [Google Scholar]
  36. W. Wei, K. Kurita, J. Kuang, A. Gao, Real-Time 3D Arm Motion Tracking Using the 6-axis IMU Sensor of a Smartwatch. IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 1–4 (2021). https://doi.org/10.1109/BSN51625.2021.9507012 [Google Scholar]
  37. N. Ruangpayoongsak, Stride Length Estimation using Fuzzy Logic and a Single Shoe Mounted IMU Sensor. Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C). 120–125 (2023). https://doi.org/10.1109/RI2C60382.2023.1035594 6 [Google Scholar]
  38. N. Petranon, S. Umchid, Design and Development of Hand Movement Detection Device for Sign Language using IMU and sEMG Sensors. Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C). 1–4 (2023). https://doi.org/10.1109/RI2C60382.2023.10356033 [Google Scholar]
  39. Li C Yu L Fei S. Real-Time 3D Motion Tracking and Reconstruction System Using Camera and IMU Sensors. IEEE Sens J. 19, 15, 6460–6466 (2019). https://doi.org/10.1109/JSEN.2019.2907716 [Google Scholar]
  40. N. Verdel et al., Time Synchronization in Wireless IMU Sensors for Accurate Gait Analysis During Running. IEEE International Workshop on Sport, Technology and Research (STAR). 126–129 (2023). https://doi.org/10.1109/STAR58331.2023.1030265 2 [Google Scholar]
  41. M. Ajčević et al., Assessment of mobility deficit and treatment efficacy in adhesive capsulitis by measurement of kinematic parameters using IMU sensors. IEEE International Symposium on Medical Measurements and Applications (MeMeA). 1–5 (2020). https://doi.org/10.1109/MeMeA49120.2020.91371 57 [Google Scholar]
  42. H. M. Saputra et al., Effect of IMU Sensor Positioning on 1-DOF Angle Measurement Accuracy for Robotic Charging Station (RoCharg- v1) Manipulator. International Conference on Computer, Control, Informatics and its Applications (IC3INA). 96–101 (2023). https://doi.org/10.1109/IC3INA60834.2023.10285 798 [Google Scholar]
  43. H. E. Kiran, A. Akgul, O. Yildiz, E. Deniz, Lightweight encryption mechanism with discrete- time chaotic maps for Internet of Robotic Things. Integration. 93, 102047 (2023). https://doi.org/10.1016/j.vlsi.2023.06.001 [Google Scholar]
  44. H. Mohamad, S. Ozgoli, Online gait generator for lower limb exoskeleton robots: Suitable for level ground, slopes, stairs, and obstacle avoidance. Rob Auton Syst. 160, 104319 (2023). https://doi.org/10.1016/j.robot.2022.104319 [Google Scholar]
  45. C. Qin et al., Development and application of an intelligent robot for rock mass structure detection: A case study of Letuan tunnel in Shandong, China. International Journal of Rock Mechanics and Mining Sciences. 169, 105419 (2023). https://doi.org/10.1016/j.ijrmms.2023.105419 [Google Scholar]
  46. F. Khadivar, K. Yao, X. Gao, A. Billard, Online active and dynamic object shape exploration with a multi-fingered robotic hand. Rob Auton Syst. 166, 104461 (2023). https://doi.org/10.1016/j.robot.2023.104461 [Google Scholar]
  47. J. Zhang et al., Design of parallel multiple tuned mass dampers for the vibration suppression of a parallel machining robot. Mech Syst Signal Process. 200, 110506 (2023). https://doi.org/10.1016/j.ymssp.2023.110506 [Google Scholar]
  48. J. Lu et al., A New Calibration Method of MEMS IMU Plus FOG IMU. IEEE Sens J. 22, 9, 8728–8737 (2022). https://doi.org/10.1109/JSEN.2022.3160692 [Google Scholar]
  49. A. R. Parrish, C. S. Jao, A. M. Shkel, Stance Phase Detection for ZUPT-Aided INS Using Knee- Mounted IMU in Crawling Scenarios. IEEE Sens Lett. 6, 5, 1–4 (2022). https://doi.org/10.1109/LSENS.2022.3165760 [Google Scholar]
  50. A. Tripathi, A. K. Mondal, L. Kumar, A. P. Prathosh, ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals. IEEE Sens Lett. 6, 10, 1–4 (2022). https://doi.org/10.1109/LSENS.2022.3206307 [Google Scholar]
  51. H. Jeon, J. Min, H. Bang, W. Youn, Quaternion- Based Iterative Extended Kalman Filter for Sensor Fusion of Vision Sensor and IMU in 6-DOF Displacement Monitoring. IEEE Sens J. 22, 23, 23188–23199 (2022). https://doi.org/10.1109/JSEN.2022.3214580 [Google Scholar]
  52. H. Zhang, Y. Li, Z. Li, 6-D Spatial Localization of Wireless Magnetically Actuated Capsule Endoscopes Based on the Fusion of Hall Sensor Array and IMU. IEEE Sens J. 22, 13, 13424–33 (2022). https://doi.org/10.1109/JSEN.2022.3175919 [Google Scholar]

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