Tek Serbestlik Dereceli Bir Teleoperasyon Sisteminde Kontrol Yöntemlerinin Performans Karşılaştırılması

Yıl 2019, Cilt: 31 Sayı: 2, 507 - 517, 27.09.2019

Tayfun Abut Servet Soygüder

https://doi.org/10.35234/fumbd.563109

Cited By: 1

Öz

Teleoperasyon sistemleri insan-robot etkileşimini(HRI) sağlayan sistemler olarak tanımlanmaktadır. Bu sistemlerin kontrolünün ilk olarak benzetim ortamında gerçekleştirilmesi, gerçek ortamda yapılacak deneyler öncesinde ve algoritma geliştirme aşamalarında tespit edilen hataların önlenmesi açısından önem taşımaktadır. Bu sistemlerin performans değerlendirilmelerinde konum ve kuvvet kontrolü önemli parametrelerdir. Bu çalışmada tek serbestlik dereceli ana (master) ve bağımlı(slave) robottan oluşan teleoperasyon sisteminin kontrolü hedeflenmiştir. Tek serbestlik dereceli robotların dinamik modelleri elde edilmiştir. Ayrıca bağımlı robotun hareketleri görselleştirmek için sanal ortamda görsel bir arayüz tasarlanmıştır. Bulanık mantık(Fuzzy Logic), PD tabanlı hesaplanmış tork kontrol(PD based-CTC) ve klasik PID kontrol yöntemleri kullanılarak sistemin iki yönlü gerçekleştirilmiştir. Bu yöntemler benzetim ortamında gerçekleştirilerek sonuçlar grafikler ve tablo şeklinde verilmiş ve irdelenmiştir.

Anahtar Kelimeler

Teleoperasyon, Tek Serbestlik Dereceli Robot, Bulanık Mantık, PD-CTC, PID

Destekleyen Kurum

Fırat Üniversitesi Bilimsel Araştırma Projeleri (FÜBAP)

Proje Numarası

MF.13.15

Teşekkür

Bu çalışma Fırat Üniversitesi Bilimsel Araştırma Projeleri (FÜBAP) 2015, MF.13.15 ’nolu proje kapsamında desteklenmiştir.

Kaynakça

• 1. Tsui, K. M., & Yanco, H. A. (2007), Assistive, surgical, and rehabilitation robots from the perspective of medical and healthcare professionals. In Proceedings of the AAAI Workshop on Human Implications of Human-Robot Interaction (pp. 34–39). Vancouver, Canada: AAAI Press
• 2. Abut, T., Soyguder, S. (2017), Real-time control of bilateral teleoperation system with adaptive computed torque method. Industrial Robot: An International Journal, 44(3), 299-311.
• 3. Abut, T., & Soygüder, S. (2018), Haptic industrial robot control and bilateral teleoperation by using a virtual visual interface. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
• 4. H. Kozima, Y. Yasuda, C. Nakagawa, (2007), Social interaction facilitated by a minimally-designed robot: Findings from longitudinal therapeutic practices for autistic children, Proc. 16th IEEE Int. Symp. Robot Hum. interact. Commun., Jeju Island pp. 599–604.
• 5. Abut, T , Soygüder, S . (2018). Interface Design and Performance Analysis for a Haptic Robot. Muş Alparslan Üniversitesi Fen Bilimleri Dergisi, 6 (2), 553-560. DOI : 10.18586/msufbd.468332.
• 6. J. Casper and R. Murphy, (June 2003), “Human-robot interaction during the robotassisted urban search and rescue effort at the world trade center,” IEEE Trans. Syst. Man, Cybern. B, vol. 33, pp. 367–385.
• 7. Wilcox, S. Nikolaidis, J. Shah, (2012) , Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing, Proc. Robotics Sci. Syst., Sydney p. 441.
• 8. T. Fong, C. Thorpe, C. Baur, (2003), Collaboration, dialogue, human-robot interaction, Robotics Res. 6, 255–266.
• 9. A. Holroyd, C. Rich, C.L. Sidner, B. Ponsler, (2011) Generating connection events for human-robot collaboration, Proc. 20th IEEE Int. Symp. Robot Hum. Interact. Commun., Atlanta ,pp. 241–246.
• 10. Carignan, C., Tang, J., & Roderick, S. (2009, October). Development of an exoskeleton haptic interface for virtual task training. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3697-3702). IEEE.
• 11. Chen, J. Y. C., Haas, E. C., & Barnes, M. J. (2007). Human performance issues and user interface design for teleoperated robots. IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and Reviews, 37, 1231–1245.
• 12. P. Strom, L. Hedman, L. Sarna, A. Kjellin, T. Wredmark, L. Fellander-Tsai, (2006),Early exposure to haptic feedback enhances performance in surgical simulator training: A prospective randomized crossover study in surgical residents, Surg. Endosc. 20(9), 1383–1388.
• 13. Katsura S., Iida W., Ohnishi K.(2005), Medical mechatronics An application to haptic forceps, Annual Reviews in Control, 29:2 237-245.
• 14. C. Basdogan, S. De, J. Kim, M. Muniyandi, M.A. Srinivasan, (2004), Haptics in minimally invasive surgical simulation and training, IEEE Comput. Graph. Appl. 24(2), 56–64.
• 15. Andrew M. Wollacott, Kenneth M. Merz Jr., (2007) “Haptic applications for molecular structure manipulation”, Journal of Molecular Graphics and Modelling Cilt 25, Sayı 6, 801-805.
• 16. M. Zyda and J. Sheehan, (1997.), “Modeling and simulation: Linking entertainment and defense,” National Research Council, Computer Science and Telecommunications Board Report.
• 17. Yan, J., & Salcudean, S. E. (1996). Teleoperation controller design using H/sub/spl infin//-optimization with application to motion-scaling. IEEE Transactions on control systems technology, 4(3), 244-258.
• 18. Kikuchi, J., Takeo, K., & Kosuge, K. (1998, May). Teleoperation system via computer network for dynamic environment. In Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146) (Vol. 4, pp. 3534-3539). IEEE.
• 19. Park, J. H., & Cho, H. C. (1999, September). Sliding-mode controller for bilateral teleoperation with varying time delay. In 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No. 99TH8399) (pp. 311-316). IEEE.
• 20. Youjun Xiong, Shiqi Li, Ming Xiet,(2006), ‘Predictive display and interaction of telerobots based on augmented reality.’ Robotica, Vol.24, pp.447‐453.
• 21. Itoh, T., Yudate, K., Ito, S., & Matsui, T. (2003). New predictive display method of motion and force information for network teleoperation without using virtual environment model. In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453) (Vol. 3, pp. 2815-2822). IEEE.
• 22. Huijun Li, Aiguo Song,(2007), ‘Virtual‐environment modeling and correction for force‐reflecting teleoperation with time delay.’ IEEE Trans. On Industrial Electronics, Vol.54(2), pp1277‐1233.
• 23. Xiong Lu, Aiguo Song,( 2008), ‘Stable haptic rendering with detailed energy‐compensating control.’ Computers &Graphics, Vol.32(5), pp561‐567.
• 24. Sirouspour, S., Shahdi,A. (2012), Adaptive control of bilateral teleoperation with time delay. Int. J. Intell. Mechatron. Robot. 2(1), 1–27.
• 25. Abbink, D.A., Boessenkool, H., Heemskerk, C.J.M., Koning, J.F., Wildenbeest, J.G.W. (2013): How operator admittance affects the response of a teleoperation system to assistive forces—a model analytic study and simulation. Fusion Eng. Des. 88(9–10), 2001–2005.
• 26. Soyguder, S., & Abut, T. (2016). Haptic industrial robot control with variable time delayed bilateral teleoperation. Industrial Robot: An International Journal, 43(4), 390-402.
• 27. Jafari, B. H., & Spong, M. W. (2017). Passivity-based switching control in teleoperation systems with time-varying communication delay. In 2017 American Control Conference (ACC) (pp. 5469-5475). IEEE.
• 28. Abut, T , Soygüder, S . (2018). Zaman Gecikmeli İnsan-Makine Etkileşimli Teleoperasyon Sisteminin Kontrolü. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 30(1), 193-202.
• 29. Hazewinkel, M. (Ed.) (2001), “Lagrange equations (in mechanics)”, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4.
• 30. Zadeh, L.A. (1965), “Fuzzy sets”, Information and Control, Vol. 8 No. 3, pp. 338-353.
• 31. Zadeh, L.A. (1988), “Fuzzy logic”, Computer, Vol. 21 No. 4, pp. 83-93.
• 32. Abut, T, Soyguder, S, (2019) "Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum", Industrial Robot: the international journal of robotics research and application, Vol. 46 Issue: 1, pp.159-170, https://doi.org/10.1108/IR-10-2018-0206.
• 33. Ziegler, J.B. and Nichols, N.B. (1942), “The classic original paper: Optimum settings for automatic controllers”, ASME Transactions, Vol. 64, pp. 759-768.
• 34. Middleton, R.H. and Goodwin, G.C. (1988), “Adaptive computed torque control for rigid link manipulators”, Systems and Control Letter, Vol. 10, pp. 9-16.
• 35. Tuong, D.N., Seeger, M. and Peters, J. (2008), “Computed torque control with nonparametric regression models”, Proceedings of the 2008 American Control Conference (ACC), Seattle, WA.