Development of control algorithm for autonomous intelligent robotic complex (AIRTC)

Development of control algorithm for autonomous intelligent robotic complex (AIRTC)

Igor A. Kozulin
PhD in Physics and Mathematics, Higher College of Informatics at Novosibirsk State University (HCI NSU), Deputy Director, 3, Russkaya ul., Novosibirsk, 630058, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it., ORCID: 0000-0001-7961-5531

Andrey N. Chernyavskiy
HCI NSU, Department of Intelligent Systems of Thermophysics, Assistant, 3, Russkaya ul., Novosibirsk, 630058, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Alexander D. Nazarov
Doctor of Technical Sciences, HCI NSU, Department of Intelligent Systems of Thermophysics, Associate Professor, 3, Russkaya ul., Novosibirsk, 630058, Russia, This email address is being protected from spambots. You need JavaScript enabled to view it.


Received April 26, 2023

Abstract
The work is dedicated to the development of a control algorithm for the autonomous intelligent robotic complex (AIRTC). The unmanned ground vehicle consists of a corpus that contains batteries, motor wheels, sensors, video cameras, on-board computer for processing data from sensors and video cameras. The general dimensions of the autonomous mobile robotic platform are 780x650x550 mm. The small size of the mobile platform allows it to be used in small areas, where the use of other vehicles is economically unprofitable. The key role in the autonomous mode of operation of the robotic complex is intended to the use of machine learning technologies to realize the possibility of performing the task assigned to the autonomous robotic platform in indoor and outdoor areas. An algorithm has been developed to control the basic platform of an autonomous robotic technical complex and to detect key obstacles automatically using a neural network. The accuracy of detecting the eight designated classes of obstacles was 85%.

Key words
Unmanned ground vehicle, artificial Intelligence, deep learning.

Acknowledgements
The work was supported by the Innovation Assistance foundation «Start» program, contract 115ГС1ЦТС10-D5/61788 from 19.10.2020 to 18.03.2022, robotics and sensorics.

DOI
10.31776/RTCJ.12106

Bibliographic description
Kozulin, I.A., Chernyavskiy, A.N. and Nazarov, A.D. (2024), "Development of control algorithm for autonomous intelligent robotic complex (AIRTC)", Robotics and Technical Cybernetics, vol. 12, no. 1, pp. 46-54, DOI: 10.31776/RTCJ.12106. (in Russian).

UDC identifier
62-51

References

  1. Kurkin, A.A. et al., Nizhny Novgorod State Technical University n.a. R.E. Alekseev (2017), Avtonomnyj mobil'nyj robototehnicheskij kompleks [Autonomous mobile robotic complex], Pat. no. RU 2632342.
  2. Saveliev, A.I., Kharkov, I.Yu., Pavlyuk, N.A. and Karpov, A.A., SPC RAS (2019), Mobil'naja avtonomnaja robototehnicheskaja platforma s blochnoj izmenjaemoj strukturoj [Mobile autonomous robotic platform with block variable structure], Pat. no. RU 2704048.
  3. Gorshunov, A.A. et al. (2018), “Development of a swiveling chassis and navigation system selection for an experimental mobile robot model”, Modern high-tech technologies, no. 8, pp. 59-65. (in Russian).
  4. Sheremet, I.B. et al. (2016), “On the need to develop a concept for the construction and use of autonomous robotic complexes for military purposes”, Extreme Robotics, vol. 1, no. 1, pp. 35-39. (in Russian).
  5. Fu, Y. et al. (2019), “Neural network-based learning from demonstration of an autonomous ground robot”, Machines, 7, no. 2.
  6. Pfeiffer, M. et al. (2017), “From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots”, 2017 IEEE International Conference on Robotics and Automation (ICRA),1527-1533.
  7. Kozulin, I.A. et al. (2023), "Development a prototype of the basic platform of the autonomous intelligent robotics system (AIRS)". Robotics and Technical Cybernetics, vol. 11, no. 4, pp. 303-311, DOI: 10.31776/RTCJ.11407. (in Russian).
  8. Yakubovsky, P. (2020), Segmentation Models Pytorch, [Online], available at: https://github.com/qubvel/segmentation_models.pytorch (Accessed 4 September 2023).
  9. Kozulin, I.A., et al., Limited Liability Company "MSigma" (2022), Bazovaja platforma avtonomnogo intellektual'nogo robototehnicheskogo kompleksa (AIRTK) [Basic platform of an autonomous intelligent robotic complex (AIRC)], Pat. no. RU 2764910 C1.
  10. Okunev, A.G. et al., Novosibirsk State University (2020), Avtonomnaja mobil'naja robototehnicheskaja platforma dlja ochistki snega [Autonomous mobile robotic platform for snow removal], Pat. no. RU 2730666 C1.