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Journal Information-measuring and Control Systems №4 for 2022 г.
Article in number:
Coordinated control of the formation of a group of UAVs with ensuring trajectory safety in the form of desired positions on spatial trajectories (part 1)
Type of article: scientific article
DOI: https://doi.org/10.18127/j20700814-202204-03
UDC: 623.746:517.977
Authors:

I.P. Titkov1, А. Karpunin2

1,2Bauman Moscow State Technical University (Moscow, Russia)

Abstract:

The use of UAVs groups for solving modern practical problems of performing multi-angle shooting, territory surveillance, monitoring the perimeter, detecting threats, and conducting search and rescue operations can increase the efficiency, speed up and improve the quality of solving these problems. They can be reduced to the task of constructing a given spatial configuration (formation) by a group of UAVs. The fundamental problem of the group use of UAVs with ensuring trajectory safety due to the possible intersection of the trajectories of movement and the collision of the UAV on all the features of the functions of the group. Coordinated control is to ensure the safe movement of the UAV from positions in the initial formation to positions in the final formation. A solution to the problem of coordinated control of the formation of a group of UAVs with ensuring trajectory safety in the form of desired positions on spatial trajectories is proposed. Goal is to develop a coordinated control algorithm with ensuring trajectory safety in multi-rotor UAVs group control systems in the formation problem.

An algorithm for determining the safe desired UAVs positions on piecewise linear spatial trajectories and a generalized algorithm for coordinated control of a group of UAVs with ensuring trajectory safety in the form of a control law for the desired UAVs positions on spatial trajectories have been developed. The results of the analysis of the solution of the problem are presented.

The developed generalized algorithm does not require a mathematical model of the control object, but only the ability of the UAV to follow along the assigned trajectory and hover in the position specified on it with a known accuracy; allows to get a solution in a predictable finite time in the case of correct initial data. The implementation of the algorithm for determining safe desired positions allows calculating safe desired positions in less than one second on 2048 linear trajectories or on 256 polygonal chains of 7 sections or on 512 polygonal chains of 3 sections in single-threaded mode with the possibility of proportional acceleration in parallel computing.

Pages: 25-35
For citation

Titkov I.P., Karpunin А. Coordinated control of the formation of a group of UAVs with ensuring trajectory safety in the form of desired positions on spatial trajectories (part 1). Information-measuring and Control Systems. 2022. V. 20. № 4. P. 25−35. DOI: https://doi.org/10.18127/j20700814-202204-03 (in Russian)

References
  1. Gen K., Chulin N.A. Algoritmy stabilizatsii dlya avtomaticheskogo upravleniya traektornym dvizheniem kvadrokoptera. Nauka i Obrazovanie: Nauchnoe izdanie. 2015. № 5. S. 218−235. DOI:10.7463/0515.0771076. (in Russian)
  2. Efanov V.N., Mizin S.V., Neretina V.V. Upravlenie poletom BPLA v stroyu na osnove koordinatsii vzaimodeistviya gruppy letatelnykh apparatov. Vestnik UGATU. 2014. T. 18. № 1 (62). S. 144−121. (in Russian)
  3. Ivanov D.Ya. Metody postroeniya prostranstvennykh formatsii v gruppakh bespilotnykh letatelnykh apparatov tipa kvadrakopterov: dissertatsiya … kandidata tekhnicheskikh nauk 05.02.05 (mesto zashchity: FGAOU VO «Yuzhnyi federalnyi universitet»). 2016. 216 s. (in Russian)
  4. Beloglazov D.A., Guzik V.F., Kosenko E.Yu., Krukhmalev V.A., Medvedev M.Yu. i dr. Intellektualnoe planirovanie traektorii podvizhnykh ob'ektov v sredakh s prepyatstviyami. M.: Fizmatlit. 2014. 300 c. (in Russian)
  5. Merkulov V.I., Milyakov D.A., Samodov I.O. Optimizatsiya algoritma gruppovogo upravleniya bespilotnymi letatelnymi apparatami v sostave lokalnoi seti. Izvestiya Yuzhnogo federalnogo universiteta. Tekhnicheskie nauki. 2014. № 12 (161). S. 157−166. (in Russian)
  6. Morozova N.S. Virtualnye formatsii i virtualnye lidery v zadache o dvizhenii stroem gruppy robotov. Vestnik Sankt-Peterburgskogo universiteta. Ser. 10. Prikladnaya matematika. Informatika. Protsessy upravleniya. 2015. № 1. S. 135−149. (in Russian)
  7. Muslimov T.Z. Metody i algoritmy gruppovogo upravleniya bespilotnymi letatelnymi apparatami samoletnogo tipa: dissertatsiya … kandidata tekhnichekikh nauk 05.13.01 (mesto zashchity: FFGBOU VO «Ufimskii gosudarstvennyi aviatsionnyi tekhnicheskii universitet»). 2020. 164 c. (in Russian)
  8. Titkov I.P., Karpunin A.A. Vyyavlenie kollizii i opredelenie granits bezopasnogo sblizheniya traektorii gruppy bespilotnykh letatelnykh apparatov na osnove uslovnoi optimizatsii. URL:https://bmr.bmstu.press/preprints/847/ (data obrashcheniya 22.01.2022). (in Russian)
  9. Titkov I.P., Karpunin A.A. Reshenie zadachi ob optimalnykh realizuemykh naznacheniyakh tselevykh polozhenii BPLA i opredelenii ocherednosti dvizheniya v zadache formatsii s obespecheniem traektornoi bezopasnosti. Mezhdunar. nauchno-issledovatelskii zhurnal. 2022. № 4 (118). C. 95−109. DOI:10.23670/IRJ.2022.118.4.016. (in Russian)
  10. Alcantara A., Capitan J., Torres-Gonzalez A., Cunha R., Ollero A. Autonomous Execution of Cinematographic Shots With Multiple Drones. IEEE Access. 2020. V. 8. P. 201300−201316. DOI:10.1109/ACCESS.2020.3036239.
  11. Alonso-Mora J. et al. Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus. IEEE international conference on robotics and automation (ICRA). 2016. P. 5356−5363. DOI:10.1109/ICRA.2016.7487747.
  12. Erdogan M.E., Innocenti M., Pollini L. Obstacle Avoidance for a Game Theoretically Controlled Formation of Unmanned Vehicles. IFAC Proceedings Volumes. V. 44. № 1. 2011. P. 6023−6028. DOI:10.3182/20110828-6-IT-1002.03043.
  13. Garcia-Aunon P., Roldan J.J., Barrientos A. Monitoring traffic in future cities with aerial swarms: Developing and optimizing a behavior-based surveillance algorithm. Cognitive Systems Research. V. 54. 2019. P. 273−286. DOI:10.1016/j.cogsys.2018.10.031.
  14. Hamann B., Chen J.-L. Data point selection for piecewise linear curve approximation. Computer Aided Geometric Design. 1994. V. 11(3). P. 289−301. DOI:10.1016/0167-8396(94)90004-3.
  15. Jin P.F., et al. Optimal formation control for quadrotors with collision avoidance based on dynamic constraints. Journal of Physics: Conference Series. 2019. V. 1215. № 1. 9 p. DOI:10.1088/1742-6596/1215/1/012018.
  16. Li N.H., Liu H.H. Formation UAV flight control using virtual structure and motion synchronization. IEEE American Control Conference. V. 2008. P. 1782−1787. DOI:10.1109/ACC.2008.4586750.
  17. Lissandrini N., Michieletto G., Antonello R., Galvan M., Franco A., Cenedese A. Cooperative Optimization of UAVs Formation Visual Tracking. Robotics. 2019. V. 8. № 3:52. DOI:10.3390/robotics8030052.
  18. Milyakov D.A., Verba V.S., Merkulov V.I., Plyashechnik A.S. Quadcopter active phased antenna arra. Procedia Computer Science. 2021. V. 186. P. 628−635. DOI:10.1016/j.procs.2021.04.185.
  19. Morgan D., Subramanian G., Chung S.J., Hadaegh F. Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming. The International Journal of Robotics Research. 2016. V. 35. № 10. P. 1261−1285. DOI:10.1177/0278364916632065.
  20. Ohta A. Sky magic: Drone entertainment show. ACM SIGGRAPH 2017 Emerging Technologies (SIGGRAPH '17). Article 27. 2017. DOI:10.1145/3084822.3108158.
  21. Wang G. et al. Distributed Consensus Control of Multiple UAVs in a Constrained Environment. IEEE International Conference on Robotics and Automation (ICRA). 2020. P. 3234−3240. DOI:10.1109/ICRA40945.2020.9196926.
  22. Wu F., Chen J., Liang Y. Leader-follower formation control for quadrotors. IOP Conference Series: Materials Science and Engineering. 2017. V. 187. № 1. 8 p. DOI:10.1088/1757-899X/187/1/012016.
  23. Xia C., Yudi A. Multi-UAV path planning based on improved neural network. Chinese Control And Decision Conference (CCDC). 2018. P. 354−359. DOI:10.1109/CCDC.2018.8407158. (in Russian)
Date of receipt: 26.05.2022
Approved after review: 10.06.2022
Accepted for publication: 15.07.2022