COMPARATIVE ANALYSIS OF CENTRALIZED AND DECENTRALIZED ALGORITHMS FOR THE MOVEMENT OF MULTICOPTER-TYPE UAVS
Keywords:
UAVs group, formation control, centralized algorithm, decentralized algorithm, group control
Abstract
The development of robotics makes their group application relevant for solving various
tasks. The effectiveness of performing the tasks of detecting and determining the coordinates of
objects by a group of robots significantly depends on the accuracy of maintaining a given formation.
In this regard, the task of determining motion planning algorithms that ensure the greatest
accuracy of maintaining a given formation is of practical interest. This article is devoted to the
study of the accuracy of maintaining the formation of a multicopter-type UAV group using a centralized
motion planning algorithm and a decentralized algorithm. The centralized algorithm uses
a master UAV, which transmits its coordinates to the slave UAVs. Based on the coordinates obtained
and the given framework of the formation, the guided UAVs plan their movement. In a decentralized
system, neighboring UAV groups transmit their coordinates to each other, on the basis
of which the movement of a separate UAV is planned. The accuracy of the control system is investigated
depending on the errors of the navigation system and the frequency of updating data on the
position of the leading or neighboring UAVs. It is assumed that the group's UAVs determine their coordinates in discrete moments of time using an external navigation system. Centralized and
decentralized algorithms are worked out by the same motion control system. The algorithms are
investigated in this article by numerical modeling methods. In the process of simulation, models of
kinematics, dynamics and actuators are taken into account, as well as models for the formation of
errors in the navigation system. It is shown that the de-centralized algorithm of group motion
planning provides higher accuracy compared to the centralized algorithm. However, the technical
implementation of a decentralized algorithm is more complicated from the point of view of organizing
a group communication system. In a centralized system, data transmission from the master
UAV to the slave should be implemented. In a decentralized system, it is required to implement
network communication.
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2. Sun F., Li H.,·Zhu W.,·Kurths J. Fixed-time formation tracking for multiple nonholonomic
wheeled mobile robots based on distributed observer, Nonlinear Dynamics, 2021, Vol. 106,
pp. 3331-3349.
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Theories and Applications, IEEE Transactions on Control Systems Technology, Vol. 23
(1), pp. 340-348.
4. Pack D.J., DeLima P., Toussaint G.J., York G. Cooperative control of UAVs for localization
of intermittently emitting mobile targets, IEEE Transactions on Systems, Man, and Cybernetics,
Part B (Cybernetics), 2009, Vol. 39 (4), pp. 959-970.
5. Bezruk G.G., Martynova L.A., Saenko I.B. Dinamicheskiy metod poiska antropogennykh
ob"ektov v morskom dne s ispol'zovaniem avtonomnykh neobitaemykh podvodnykh apparatov
[Dynamic Method of Searching Anthropogenic Objects in Use of Seabed with Autonomous
Underwater Vehicles], Tr. SPIIRAN [SPIIRAS Proceedings], 2018, Vol. 3 (58), pp. 203-226.
6. Shepeta A.P., Nenashev V.A. Tochnostnye kharakteristiki opredeleniya koordinat ob"ektov v
dvukhpozitsionnoy sisteme malogabaritnykh bortovykh RLS [Accuracy characteristics of object
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sistemy [Informatsionno-Upravliaiushchie Sistemy], 2020, Vol. 2, pp. 31-36.
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MultiPosition on-Board Location Systems, 2020 Wave Electronics and its Application in Information
and Telecommunication Systems (WECONF), 2020, pp. 1-5. – DOI: 10.1109/
WECONF48837.2020.9131451.
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bortovoy sisteme [Formation of Fused Images of the Land Surface from Radar and Optical
Images in Spatially Distributed On-Board Operational Monitoring Systems], Informatika i
avtomatizatsiya [Informatics and Automation], 2021, Vol. 20, No. 2, pp. 302-340.
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Structures, Autonomous Robots, 1997, No. 4, pp. 387-403.
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Proc. of the IEEE/RSJ Intern. Conf. on Intelligent Robots and Systems, 1996, pp. 132-139.
12. Morozova N.S. Virtual'nye formatsii i virtual'nye lidery v zadache o dvizhenii stroem gruppy
robotov [Virtual formations and virtual leaders in the task of moving a group of robots in formation],
Vestnik Sankt-Peterburgskogo universiteta. Seriya 10. Prikladnaya matematika //
Informatika. Protsessy upravleniya [Vestnik of Saint Petersburg University Applied Mathematics.
Computer Science. Control Processes], 2015, No. 1, pp. 135-149.
13. Endo T., Maeda R., Matsuno F. Analiz ustoychivosti roya geterogennykh robotov s
ogranichennym polem zreniya [Stability Analysis of Swarm Heterogeneous Robots with Limited
Field of View], Informatika i avtomatizatsiya [Informatics and Automation], 2020,
Vol. 19 (5), pp. 942-966.
14. Gayduk A.R., Mart'yanov O.V., Medvedev M.Yu., Pshikhopov V.Kh., Khamdan N., Farkhud A.
Neyrosetevaya sistema upravleniya gruppoy robotov v neopredelennoy dvumernoy srede [Neural
network based control system for robots group operating in 2-d uncertain environment],
Mekhatronika, avtomatizatsiya, upravlenie [Mekhatronika, Avtomatizatsiya, Upravlenie], 2020,
Vol. 21 (8), pp. 470-479.
15. Pshikhopov V.Kh., Medvedev M.Yu. Gruppovoe upravlenie dvizheniem mobil'nykh robotov v
neopredelennoy srede s ispol'zovaniem neustoychivykh rezhimov [Group control of autonomous
robots motion in uncertain environment via unstable modes], Trudy SPIIRAN [SPIIRAS
Proceedings], 2018, Issue 60, pp. 39-63.
16. Medvedev M., Pshikhopov V., Gurenko B., Hamdan N. Path planning method for mobile robot
with maneuver restrictions, Proc. of the International Conference on Electrical, Computer,
Communications and Mechatronics Engineering (ICECCME) 7-8 October 2021, Mauritius.
10.1109/ICECCME52200.2021.9591090.
17. Carlos A., Hebertt S., Jos´e A. Stability of Active Disturbance Rejection Control for Uncertain
Systems: a Lyapunov Perspective, International Journal of Robust Nonlinear Control, 2017,
Vol. 27, pp. 4541-4553. DOI: 10.1002/rnc.3812.
18. Vorotnikov V.I., Vokhmyanina A.V. Metod linearizuyushchey obratnoy svyazi v zadache
upravleniya po chasti peremennykh pri nekontroliruemykh pomekhakh [Feedback
Liniarization Method for Problem of Control of a Part of Variables in Uncontrolled Disturbances],
Tr. SPIIRAN [SPIIRAS Proceedings], 2018, Issue 6 (61), pp. 61-93.
19. Fel'dbaum A.A. O raspredelenii korney kharakteristicheskogo uravneniya sistemy regulirovaniya
[On the distribution of the roots of the characteristic equation of control systems], Avtomatika i
telemekhanika [Automation and Remote Control], 1948, No. 4, pp. 253-279.
20. Finaev V.I., Medvedev M.Yu., Pshikhopov V.K., Pereverzev V.A., Soloviev V.V. Unmanned
Powerboat Motion Terminal Control in an Environment with Moving Obstacles,
Mekhatronika, Avtomatizatsiya, Upravlenie, 2021, Vol. 22 (3), pp. 145-154. Available at:
https://doi.org/10.17587/mau.22.145-154.
21. Park B.-S., Yoo, S.-J. Adaptive Secure Control for Leader-Follower Formation of
Nonholonomic Mobile Robots in the Presence of Uncertainty and Deception Attacks, Mathematics,
2021, Vol. 9. Available at: https://doi.org/10.3390/math9182190.
22. Hirata-Acosta J., Pliego-Jiménez J., Cruz-Hernádez C., Martínez-Clark R. Leader-Follower
Formation Control of Wheeled Mobile Robots without Attitude Measurements, Applied Sciences,
2021, Vol. 11 (12), 5639. – https://doi.org/10.3390/app11125639.
23. Maghenem M., Loria A., Panteley E. Cascades-based leader-follower formation tracking and
stabilization of multiple nonholonomic vehicles, IEEE Transactions on Automatic Control,
Vol. 65 (8), pp. 3639-3646.
24. Wang Z., Wang L., Zhang H., Chen Q., Liu J. Distributed regular polygon formation control
and obstacle avoidance for non-holonomic wheeled mobile robots with directed communication
topology, IET Control Theory and Applications, 2020, Vol. 14 (9), pp. 1113-1122.
25. Sun J., Chen J. A survey on Lyapunov-based methods for stability of linear time-delay systems,
Frontiers of Computer Science, 2017, Vol. 11, pp. 555-567. Available at:
https://doi.org/10.1007/s11704-016-6120-3.
26. Hu J., Bhowmick P., Lanzon A. Group Coordinated Control of Networked Mobile Robots With
Applications to Object Transportation, IEEE Transactions on Vehicular Technology, 2021,
Vol. 70 (8), pp. 8269-8274. DOI: 10.1109/TVT.2021.3093157.
27. Arnold W.F., Laub A.J. Generalized Eigenproblem Algorithms and Software for Algebraic
Riccati Equations, Proceedings of the IEEE, 1984, Vol. 72 (12), pp. 1746-1754.
28. Pshikhopov, V., Medvedev, M. Multi-Loop Adaptive Control of Mobile Objects in Solving Trajectory
Tracking Tasks, Automation and Remote Control, 2020, Vol. 81 (11), pp. 2078-2093.
– https://doi.org/10.1134/S0005117920110090.
29. Stephen T. Thornton, Jerry B. Marion. Classical Dynamics of Particles and Systems. Brooks
Cole. 5th ed., 2003, 672 p. ISBN-10: 0534408966.
30. Pshikhopov V.Kh., Medvedev M.Yu., Gayduk A.R., Neydorf R.A., Belyaev V.E., Fedorenko R.V.,
Kostyukov V.A., Krukhmalev V.A. Sistema pozitsionno-traektornogo upravleniya robotizirovannoy
vozdukhoplavatel'noy platformoy: matematicheskaya model' [Positional trajectory control system of
a robotic aeronautical platform: mathematical model], Mekhatronika, avtomatizatsiya i upravlenie
[Mekhatronika, Avtomatizatsiya, Upravlenie], 2013, No. 6, pp. 14-21.
31. Götten F., Finger D.F., Havermann M. et al. Full configuration drag estimation of short-to-medium
range fixed-wing UAVs and its impact on initial sizing optimization, CEAS Aeronautical Journal,
2021, Vol. 12, pp. 589-603. Available at: https://doi.org/10.1007/s13272-021-00522-w.
32. Milne-Thomson L.M. Theoretical aerodynamics. Courier Corporation, 2012.
33. Li X., Qi G., Zhang L. Time-varying formation dynamics modeling and constrained trajectory
optimization of multi-quadrotor UAVs, Nonlinear Dynamics, 2021, Vol. 106, pp. 3265-3284.
Available at: https://doi.org/10.1007/s11071-021-06788-3.
34. Medvedev M., Pshikhopov V. Path Planning of Mobile Robot Group Based on Neural Networks,
Lecture Notes in Artificial Intelligence, 2020, pp. 51-62. Available at: https://doi.org/
10.1007/978-3-030-55789-8_5.
35. Ren X.X., Yang G.H. Noise covariance estimation for networked linear systems under random
access protocol scheduling, Neurocomputing, 2021, Vol. 455 (30), pp. 68-77.
36. Golnaraghi F., Kuo B.C. Automatic Control Systems. 9-th ed. JOHN WILEY & SONS, 2010,
944 p.
37. Diebold F. Elements of Forecasting (Fourth ed.). Thomson/South-Western, 2007, 366 p.
38. El-Sheimy N., Hou H., Niu X. Analysis and modeling of inertial sensors using Allan variance,
IEEE Transactions on Instrumentation and Measurement, 2008, Vol. 57 (1), pp. 140-149.
39. Consolini L., Morbidi F., Prattichizzo D., et al. Leader-follower formation control of nonholonomic
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Published
2022-04-21
Issue
Section
SECTION II. CONTROL AND SIMULATION SYSTEMS
