DECENTRALIZED CONTROL OF A GROUP OF AUTONOMOUS MOBILE OBJECTS WHEN FORMING A TRAJECTORY OF MOVEMENT
Cite as: B.K. Lebedev, O.B. Lebedev, M.I. Beskhmelnov. Decentralized control of a group of autonomous mobile objects when forming a trajectory of movement // Izvestiya SFedU. Engineering Sciences – 2024. – N. 6. - P. 177-190. doi: 10.18522/2311-3103-2024-6-177-190
Keywords:
Group of unmanned aerial vehicles, formation movement, group control, decentralized control, trajectory, alternative collective adaptation, adaptation object, swarm algorithms
Abstract
The article considers algorithms for generating unmanned aerial vehicles motion trajectories during
search and rescue and liquidation operations. The methods and algorithms for controlling the motion of a
unmanned aerial vehicles group in formation, when deployed in a line, when deployed in a rank, when
turning, in a column are described. Control is carried out using alternative collective adaptation algorithms
based on the ideas of collective behavior. The operating principles of one adaptation machine are
considered. The purpose of controlling slave robots is to minimize deviations. To implement the adaptation
mechanism, the parameters of the vector are matched with adaptation machines that model the behavior
of adaptation objects in the environment. A structure has been developed for the process of alternative
collective adaptation of parameters that control the motion of a group of unmanned aerial vehicles in
formation. Original rules for controlling parameters have been developed that have a number of advantages
over other methods: complete decentralization of control in combination with dynamic correction
of robot parameters that set the position and orientation of the robot in an absolute coordinate system,
and the linear velocity of the robot, respectively. A structure of a maneuver performed by a robot to correct
parameter deviations is proposed. Control is performed using an alternative collective adaptation algorithm
based on the ideas of collective behavior of adaptation objects, which allows for efficient processing
of emergency situations, such as agent failure, changes in the number of agents due to failure or sudden
acquisition of communication with the next agent, as well as in conditions of measurement errors and
noise that satisfy certain restrictions.
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avtomatizatsiya, upravlenie [Mechatronics, automation, control], 2020, No. 21 (8), pp. 470-479.
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neopredelennykh sredakh [Control of moving objects in certain and uncertain environments]. Moscow:
Nauka, 2011.
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osnove roevogo vzaimodeystviya [Distribution of roles in robot coalitions with limited communications
based on swarm interaction], Upravlenie bol'shimi sistemami: Sb. trudov [Control of large systems:
Collection of works], 2019, No. 78, pp. 23-45.
16. Pshikhopov V.Kh., Medvedev M.Yu., Gurenko B.V. Algoritmy adaptivnykh pozitsionno-traektornykh
sistem upravleniya podvizhnymi ob"ektami [Algorithms of adaptive position-trajectory control systems
for moving objects], Problemy upravleniya [Problems of control], 2015, No. 4, pp. 66-74.
17. Caro G.D., Ducatelle F. AntHocNet: An adaptive nature inspired algorithm for routing in mobile ad
hoc networks, European Transactions on Telecommunications, 2005, No. 16 (5), pp. 443-455.
18. Chen S., Eshaghian M.M. A fast recursive mapping algorithm. Department of computer and information
science. New Jersey, USA: New Jersey, 2013, pp. 219-227.
19. Fatemeh K.P., Reza S.N. Comparing the Performance of Genetic Algorithm and Ant Colony Optimization
Algorithm for Mobile Robot Path Planning in the Dynamic Environments with Different Complexities,
Journal of Academic and Applied Studies, 2013, Vol. 3 (2), pp. 29-44.
20. Hoefler T., Snir M. Generic Topology Mapping Strategies for Large-scale Parallel Architectures. University
of Illinois, 2011, pp. 75-85.
21. Veselov G.E., Lebedev B.K., Lebedev O.B. Management of Behavior of a Swarm of Robots Applicable
to the Tasks of Monitoring a Some Territory, Artificial Intelligence Methods in Intelligent Algorithms.
Springer, Czech Republic, 2020, Vol. 2, pp. 324-332.
22. Makarenko S.I. Robototekhnicheskie kompleksy voennogo naznacheniya sovremennoe sostoyanie i
perspektivy razvitiya [Robotic complexes for military purposes: current state and development prospects],
Sistemy upravleniya, svyazi i bezopasnosti [Control, communication and security systems], 2016, No. 2.
presledovaniya [Algorithms for controlling the motion of a mobile robot in a pursuit problem],
Informatika, telekommunikatsii i upravlenie [Computer Science, Telecommunications and Management],
2014, No. 6 (210), pp. 49-58.
2. Kotov D.V., Lebedev O.B. Metody i algoritmy upravleniya dvizheniem gruppy bespilotnykh
letatel'nykh apparatov [Methods and algorithms for controlling the movement of a group of unmanned
aerial vehicles], Integrirovannye modeli i myagkie vychisleniya v iskusstvennom intellekte: Sb.
nauchnykh trudov XII-y Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii [Integrated models and
soft computing in artificial intelligence. Collection of scientific papers of the XII International Scientific
and Technical Conference]. Kolomna: Universum, 2024, pp. 236-248.
3. Veselov G.E., Lebedev B.K., Lebedev O.B. Upravlenie dvizheniem gruppy mobil'nykh robotov v
kolonne [Control of the movement of a group of mobile robots in a column], Nauchno-prakticheskiy
zhurnal Informatizatsiya i svyaz' [Scientific and practical journal Informatization and Communication],
2021, No. 3, pp. 7-11.
4. Veselov G.E., Lebedev B.K., Lebedev O.B. Adaptivnoe upravlenie peredvizheniem kollektiva
mobil'nykh robotov razvernutykh v liniyu [Adaptive control of the movement of a team of mobile robots
deployed in a line], Integrirovannye modeli i myagkie vychisleniya v iskusstvennom intellekte: Sb.
nauchnykh trudov X-y Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii [Integrated models and
soft computing in artificial intelligence. Collection of scientific papers of the X-th International Scientific
and Technical Conference]. Smolensk: Universum, 2021, pp. 238-246.
5. Guzik V.F., Pereverzev V.A., P'yavchenko A.O., Saprykin R.V. Printsipy postroeniya ekstrapoliruyushchego
mnogomernogo neyrosetevogo planirovshchika intellektual'noy sistemy pozitsionno-traektornogo
upravleniya podvizhnymi ob"ektami [Principles of constructing an extrapolating multidimensional
neural network planner for an intelligent system of position-trajectory control of moving objects],
Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2016, No. 2 (175),
pp. 67-80.
6. Kostyukov V.A., Medvedev I.M., Medvedev M.Yu., Pshikhopov V.Kh. Chislennoe modelirovanie
roevogo algoritma planirovaniya puti v dvukhmernoy nekartografirovannoy srede [Numerical modeling
of a swarm path planning algorithm in a two-dimensional unmapped environment], Vestnik
Yuzhno-Ural'skogo gosudarstvennogo universiteta [Bulletin of the South Ural State University], 2024,
Vol. 16, No. 2, pp. 26-40.
7. Karpenko A.P. Sovremennye algoritmy poiskovoy optimizatsii. Algoritmy, vdokhnovlennye prirodoy:
ucheb. posobie [Modern algorithms of search engine optimization. Algorithms inspired by nature: a tutorial].
3rd ed. Moscow: Izd-vo MGTU im. N.E. Baumana, 2021, 448 p.
8. Kuznetsov A.V. Model' dvizheniya i vzaimodeystviya sistemy intellektual'nykh agentov [Model of
movement and interaction of a system of intelligent agents], Vestnik VGU. Seriya: Sistemnyy analiz i
informatsionnye tekhnologii [VSU Bulletin. Series: Systems analysis and information technology],
2018, No. 2, pp. 130-138.
9. Muslimov T.Z., Munasypov R.A. Detsentralizovannoe gruppovoe nelineynoe upravlenie stroem
bespilotnykh letatel'nykh apparatov samoletnogo tipa [Decentralized group nonlinear control of a formation
of aircraft-type unmanned aerial vehicles], Mekhatronika, avtomatizatsiya, upravlenie [Mechatronics,
automation, control], 2020, Vol. 21, No. 1, pp. 43-50.
10. Beloglazov D.A., Guzik V.F., Medvedev M.Yu., Pshikhopov V.Kh., P'yavchenko A.O., Saprykin R.V.,
Solov'ev V.V., Finaev V.I. Intellektual'nye tekhnologii planirovaniya peremeshcheniy podvizhnykh ob"ektov
v trekhmernykh nedeterminirovannykh sredakh [Intelligent technologies for planning the movements of
moving objects in three-dimensional non-deterministic environments]. Moscow: Nauka, 2017, 232 p.
11. Neydorf R.A., Polyakh V.V., Chernogorov I.V., Yarakhmedov O.T. Issledovanie evristicheskikh algoritmov v
zadachakh prokladki i optimizatsiya marshrutov v srede s prepyatstviyami [Study of heuristic algorithms in
problems of route planning and optimization in an environment with obstacles], Izvestiya YuFU.
Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2016, No. 3 (176), pp. 127-143.
12. Pshikhopov V.Kh. i dr. Intellektual'noe planirovanie traektoriy podvizhnykh ob"ektov v sredakh s
prepyatstviyami [Intelligent planning of trajectories of moving objects in environments with obstacles],
ed. by V.Kh. Pshikhopova. Moscow: Fizmatlit, 2014.
13. 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
control system for a group of robots in an uncertain two-dimensional environment], Mekhatronika,
avtomatizatsiya, upravlenie [Mechatronics, automation, control], 2020, No. 21 (8), pp. 470-479.
14. Pshikhopov V.Kh., Medvedev M.Yu. Upravlenie podvizhnymi ob"ektami v opredelennykh i
neopredelennykh sredakh [Control of moving objects in certain and uncertain environments]. Moscow:
Nauka, 2011.
15. Ivanov D.Ya. Raspredelenie roley v koalitsiyakh robotov pri ogranichennykh kommunikatsiyakh na
osnove roevogo vzaimodeystviya [Distribution of roles in robot coalitions with limited communications
based on swarm interaction], Upravlenie bol'shimi sistemami: Sb. trudov [Control of large systems:
Collection of works], 2019, No. 78, pp. 23-45.
16. Pshikhopov V.Kh., Medvedev M.Yu., Gurenko B.V. Algoritmy adaptivnykh pozitsionno-traektornykh
sistem upravleniya podvizhnymi ob"ektami [Algorithms of adaptive position-trajectory control systems
for moving objects], Problemy upravleniya [Problems of control], 2015, No. 4, pp. 66-74.
17. Caro G.D., Ducatelle F. AntHocNet: An adaptive nature inspired algorithm for routing in mobile ad
hoc networks, European Transactions on Telecommunications, 2005, No. 16 (5), pp. 443-455.
18. Chen S., Eshaghian M.M. A fast recursive mapping algorithm. Department of computer and information
science. New Jersey, USA: New Jersey, 2013, pp. 219-227.
19. Fatemeh K.P., Reza S.N. Comparing the Performance of Genetic Algorithm and Ant Colony Optimization
Algorithm for Mobile Robot Path Planning in the Dynamic Environments with Different Complexities,
Journal of Academic and Applied Studies, 2013, Vol. 3 (2), pp. 29-44.
20. Hoefler T., Snir M. Generic Topology Mapping Strategies for Large-scale Parallel Architectures. University
of Illinois, 2011, pp. 75-85.
21. Veselov G.E., Lebedev B.K., Lebedev O.B. Management of Behavior of a Swarm of Robots Applicable
to the Tasks of Monitoring a Some Territory, Artificial Intelligence Methods in Intelligent Algorithms.
Springer, Czech Republic, 2020, Vol. 2, pp. 324-332.
22. Makarenko S.I. Robototekhnicheskie kompleksy voennogo naznacheniya sovremennoe sostoyanie i
perspektivy razvitiya [Robotic complexes for military purposes: current state and development prospects],
Sistemy upravleniya, svyazi i bezopasnosti [Control, communication and security systems], 2016, No. 2.
Published
2025-01-19
Issue
Section
SECTION III. COMPUTING AND INFORMATION MANAGEMENT SYSTEMS
