Unmanned aerial vehicle control using myographic interface and feedback system

Unmanned aerial vehicle control using myographic interface and feedback system

Rinat R. Galin
PhD in Technical Sciences, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Senior Research Scientist, 65, Profsoyuznaya ul., Moscow, 117997, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-6429-7868

Roman V. Meshcheryakov
Doctor of Technical Science, Professor, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Chief Research Scientist, 65, Profsoyuznaya ul., Moscow, 117997, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-1129-8434

Yaroslav A. Turovskiy
PhD in Medical Sciences, Doctor of Technical Science, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Control Based on Incomplete Data, Leading Research Scientist, 65, Profsoyuznaya ul., Moscow, 117997, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0002-5290-885X

Saniya B. Galina
V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Research Scientist, 65, Profsoyuznaya ul., Moscow, 117997, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-5242-0996

Anastasia O. Iskhakova
PhD in Technical Sciences, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Senior Research Scientist, 65, Profsoyuznaya ul., Moscow, 117997, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-8358-298X

Vladimir I. Venets
PhD in Physics and Mathematics, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Senior Research Scientist, 65, Profsoyuznaya ul., Moscow, 117997, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.


Received March 11, 2025

Abstract
The work is devoted to the study of UAV control systems based on myographic interface, which allows controlling the vehicle using muscle signals, providing intuitive and natural human-machine interaction, as well as to the evaluation of operator psychological factors affecting control. A general scheme of the UAV control system with feedback is presented. The authors conducted an experiment on practicing scripted UAV flights using myographic interface and classical keyboard control. During considering the relative number of errors, it was found that the number of errors for the myographic interface is higher compared to the keyboard interface, but the dependence is due to the unconventional and new way of controlling the UAV, operating in discrete mode in contrast to the keyboard interface. The inverse relationship of the expression of the visual channel of perception with the number of errors has been established regardless of the type of interface used.

Key words
Myographic interface, control system, psychological factors, unmanned aerial vehicle.

Acknowledgements
The reported study was funded by the Russian Science Foundation according to the research project No. 23-19-00664, https://rscf.ru/en/project/23-19-00664/.

Bibliographic description
Galin, R.R. et al. (2025), "Unmanned aerial vehicle control using myographic interface and feedback system", Robotics and Technical Cybernetics, vol. 13, no. 2, pp. 135-142, EDN: HADIKO. (in Russian).

EDN
HADIKO

UDC identifier
623.746-519:004.8

References

  1. Vorotnikov, S.A. (2005), Informacionnye ustrojstva robototehnicheskih sistem [Information devices of robotic systems], Bauman Moscow State Technical University. N.E. Bauman Publ., Moscow, Russia, p. 384. (in Russian).
  2. Yushchenko, A.S. (2018), “Human and robot - ergonomic problems of collaborative robotics”, Mir psihologii, 201, 4(96), pp. 86-102. (in Russian).
  3. Kandrashina, E.Ju., Litvinceva, L.V., Pospelov, D.A. (1989), Predstavlenie znanij o vremeni i prostranstve v intellektual'nyh sistemah [Representation of knowledge about time and space in intelligent systems], Nauka publ., p. 326. (in Russian).
  4. Turovskiy, Ya.A. (2017), “Some physiological aspects of prospective human-computer interfaces”, In Proceedings of the 23rd Congress of the I.P. Pavlov Physiological Society, Voronezh, September, 18–22, Istoki Publ., p. 1050-1052. (in Russian).
  5. Dwivedi, A. et al. (2019), “Combining electromyography and fiducial marker based tracking for intuitive telemanipulation with a robot arm hand system”, In Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), October 14–18, 2019, New Delhi, India, pp. 1–6, DOI: 10.1109/RO-MAN46459.2019.8956456.
  6. Shieff, D. et al. (2021), “An Electromyography Based Shared Control Framework for Intuitive Robotic Telemanipulation”, In Proceedings of the 2021 20th International Conference on Advanced Robotics (ICAR), December 6–10, 2021, Ljubljana, Slovenia, pp. 806–811, DOI: 10.1109/ICAR53236.2021.9659463.
  7. Wang, Z. et al. (2020), “Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: A case study”, Eng. Phys, vol. 75, pp. 45–48, DOI: 10.1016/j.medengphy.2019.11.005.
  8. Wu, H. et al. (2019), “A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals”, In Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, pp. 124–131, DOI: 10.1109/ITNEC.2019.8729426.
  9. Nsugbe, E. et al. (2020), “Gesture recognition for transhumeral prosthesis control using EMG and NIR”, IET Cyber-systems and Robotics, pp. 122–131, DOI: 10.1049/iet-csr.2020.0008.
  10. Dwivedi, A., Groll, H. and Beckerle, P. (2022), “A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding”, Sensors, 22(17), p. 6319, DOI: 10.3390/s22176319.
  11. Ortenzi, V. et al. (2015), “Ultrasound imaging for hand prosthesis control: A comparative study of features and classification methods”, In Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), August 11–14, 2015, Singapore, pp. 1–6, DOI: 10.3390/s22176319.
  12. Praagman, M. et al. (2023), “Muscle oxygen consumption, determined by NIRS, in relation to external force and EMG”, Biomech, vol. 36, pp. 905–912, DOI: 10.1016/s0021-9290(03)00081-2.
  13. Turovskiy, Ya.A., Borzunov, S.V. and Vahtin, A.A. (2022), “Algorithm of correction of statistical estimation taking into account the effect of multiple comparisons on the basis of grouping theses results”, Programmnaja inzhenerija, vol. 13, no. 3, pp. 148-152. (in Russian).