10.14489/vkit.2022.03.pp.003-014

DOI: 10.14489/vkit.2022.03.pp.003-014

Евдокименков В. Н., Хохлов С. В.
ПЛАНИРОВАНИЕ ГРУППОВЫХ ДЕЙСТВИЙ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ В РАСПРЕДЕЛЕННОЙ СИСТЕМЕ ИНТЕЛЛЕКТУАЛЬНОГО УПРАВЛЕНИЯ
(с. 3-14)

Аннотация. Рассмотрены подходы к реализации алгоритмов, обеспечивающих планирование групповых действий беспилотных летательных аппаратов (БЛА) в составе распределенной системы интеллектуального управления, обобщающие современное состояние исследований в данной предметной области и собственные результаты авторов. Даны описания алгоритмов планирования групповых действий БЛА, основу которых составляют математические модели и формализованные критерии для оценки эффективности коллективных действий. В этом случае задача планирования, как правило, может быть сведена к задаче целочисленного линейного или нелинейного программирования. Показана возможность использования технологий искусственного интеллекта для целей планирования групповых действий БЛА. Отдельное внимание уделено проблеме планирования групповых действий БЛА в рамках децентрализованной стратегии стайного управления.

Ключевые слова: беспилотный летательный аппарат; групповые действия; планирование; стратегии управления; координированное управление; оптимизация; интеллектуальное управление.

Evdokimenkov V. N., Hohlov S. V.
PLANNING OF GROUP ACTIONS OF UNMANNED AERIAL VEHICLES IN A DISTRIBUTED INTELLIGENT CONTROL SYSTEM
(pp. 3-14)

Abstract. The actual progress achieved in the field of unmanned flying vehicles makes it possible to use Unmanned Aerial Vehicles (UAVs) to solve various tasks in the civil and defense areas. As a result, UAV groups require scheduling of their actions at various stages of their mission. In their previous publications the authors suggested the architecture of a distributed intellectual control system for the implementation of UAVs collective actions. It was demonstrated, that one of the most important functions of such an intellectual control system is the so called pre-flight scheduling of UAVs actions within a group. The article discusses the approaches to the implementation of algorithms that ensure the pre-flight scheduling of UAVs actions in the frames of a distributed intellectual control system, summarizing the current state of research in this area as well as the authors’ own results. The most important task to be solved at the stage of a UAV group’s actions pre-flight scheduling is to determine the types of UAVs involved in the implementation of the target task. An approximate solution to this problem is proposed, basing on the use of analytical probabilistic models to evaluate the group actions efficiency. The use of such models makes it possible to determine the optimal number of UAVs in the group, to justify the requirements for their survivability, the characteristics of both on-board optoelectronic means and weapons. Algorithms for scheduling the UAV group actions are described, which are based on both mathematical models and formalized criteria for evaluation of the collective actions efficiency. In this case, the planning task, as a rule, can be reduced to the problem of integer linear or nonlinear programming. The possibility of using artificial intelligence technologies for the purposes of the UAV group actions scheduling is discussed. Special attention is paid to the problem of planning the UAV group actions within the framework of a decentralized strategy of flock management.

Keywords: Unmanned aerial vehicle; Group actions; Planning; Control strategies; Coordinated control; Optimization; Intelligent control.

+ - Информация об авторах (About the Authors) Click to collapse

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В. Н. Евдокименков (Московский авиационный институт (национальный исследовательский университет), Москва, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript
С. В. Хохлов (ФГУП «Государственный научно-исследовательский институт авиационных систем» ГНЦ РФ, Москва, Россия)

Eng

V. N. Evdokimenkov (Moscow Aviation Institute (National Research University), Moscow, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript
S. V. Hohlov (State Research Institute of Aviation Systems State Scientific Center of Russian Federation, Moscow, Russia)

+ - Библиографический список (References) Click to collapse

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1. Евдокименков В. Н., Красильщиков М. Н., Оркин С. Д. Управление смешанными группами пилотируемых и беспилотных летательных аппаратов в условиях единого информационно-управляющего поля. М.: Изд-во МАИ, 2015. 271 с.
2. Kress M., Baggesen A., Gofer E. Probability Modeling of Autonomous Unmanned Combat Aerial Vehicles (UCAVs) // Military Operations Research. 2006. V. 11, No. 4. Р. 5 – 24. DOI 10.5711/morj.11.4.5
3. Aggarwal R., Kumar M., Keil R. E., Rao A. V. Chance-Constrained Path Planning in Narrow Spaces for a Dubins Vehicle // International Robotics & Automation Journal. 2021. V. 7, No. 2. P. 46 – 61. DOI 10.15406/iratj.2021.07.00277
4. LaValle S. M. Planning Algorithms. Cambridge University Press, 2006. 1007 p.
5. Dubins L. E. On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents // American Journal of Mathematics. 1957. V. 79, No. 3. P. 497 – 516. DOI 10.2307/2372560
6. Task Allocation for Wide Area Search Munitions with Variable Path Length / C. Schumacher et al. // Proc. of the American Control Conf. June, Denver. Colorado. 2003. V. 4. P. 3472 – 3477. DOI 10.1109/ACC.2003.1244069
7. Distributed Control for Multiple UAVs with Strongle Coupled Tasks / P. Chandler et al. // Proc. of the AIAA Guidance, Navigation, and Control Conference and Exhibit. August, Austin, Texas. 2003. AIAA 2003-5799. DOI 10.2514/6.2003-5799
8. Turra D., Pollini L., Innocenti M. Real-Time Unmanned Vehicles Task Allocation with Moving Targets // Proc. of the AIAA Guidance, Navigation, and Control Conference and Exhibit. August, Providence. Rhode Island. 2004. AIAA 2004-5253. DOI 10.2514/6.2004-5253
9. Jin Y., Minai A. A., Polycarpou M. M. Cooperative Real-Time Search and Task Allocation in UAV Teams // Proc. of the IEEE Conf. on Decision and Control. December 2003, Maui, Hawaii. 2003. V. 1. P. 7 – 12. DOI 10.1109/CDC.2003.1272527
10. Rusmevichientong P., Van Roy B. Decentralized Decision-Making in a Large Team with Local Information // Games and Economic Behavior. 2003. V. 43, No. 2. P. 266 – 295. DOI 10.1016/S0899-8256(03)00006-X
11. Winstrand M. Mission Planning and Control of Multiple UAV’s. Scientific Report. No. FOI-R-1382-SE Swedesh Defence Research Agency, 2004. 52 p.
12. Zhu R., Sun D., Zhou Z. Cooperation Strategy of Unmanned Air Vehicles for Multitarget Interception // Journal Guidance. 2005. V. 28, No. 5. P. 1068 – 1072. DOI 10.2514/1.14412
13. Chandler P. R., Pachter M., Rassmussen S. UAV Cooperative Control // Proc. of the IEEE American Control Conference. New York. 2001. V. 1. P. 50 – 55. DOI 10.1109/ACC.2001.945512
14. Takahashi O., Schilling R. Motion Planning in a Plane Using Voronoi Diagrams // IEEE Transaction on Robotics and Automation. 1989. V. 5, No. 2. DOI 10.1109/70.88035
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Eng

1. Evdokimenkov V. N., Krasil'shchikov M. N., Orkin S. D. (2015). Management of mixed groups of manned and unmanned aerial vehicles in a single information and control field. Moscow: Izdatel'stvo MAI. [in Russian language]
2. Kress M., Baggesen A., Gofer E. (2006). Probability Modeling of Autonomous Unmanned Combat Aerial Vehicles (UCAVs). Military Operations Research, Vol. 11, (4), pp. 5 – 24. DOI 10.5711/morj.11.4.5
3. Aggarwal R., Kumar M., Keil R. E., Rao A. V. (2021). Chance-Constrained Path Planning in Narrow Spaces for a Dubins Vehicle. International Robotics & Automation Journal, Vol. 7, (2), pp. 46 – 61. DOI 10.15406/iratj.2021.07.00277
4. LaValle S. M. (2006). Planning Algorithms. Cambridge University Press.
5. Dubins L. E. (1957). On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents. American Journal of Mathematics, Vol. 79, (3), pp. 497 – 516. DOI 10.2307/2372560
6. Schumacher C. et al. (2003). Task Allocation for Wide Area Search Munitions with Variable Path Length. Proceedings of the American Control Conference, Vol. 4, pp. 3472 – 3477. Denver. DOI 10.1109/ACC.2003. 1244069
7. Chandler P. et al. (2003). Distributed Control for Multiple UAVs with Strongle Coupled Tasks. Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Austin. AIAA 2003-5799. DOI 10.2514/6.2003-5799
8. Turra D., Pollini L., Innocenti M. (2004). Real-Time Unmanned Vehicles Task Allocation with Moving Targets. Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. August, Providence. AIAA 2004-5253. DOI 10.2514/6.2004-5253
9. Jin Y., Minai A. A., Polycarpou M. M. (2003). Cooperative Real-Time Search and Task Allocation in UAV Teams. Proceedings of the IEEE Conference on Decision and Control, Vol. 1, pp. 7 – 12. Maui. DOI 10.1109/CDC.2003.1272527
10. Rusmevichientong P., Van Roy B. (2003). Decentralized Decision-Making in a Large Team with Local Information. Games and Economic Behavior, Vol. 43, (2), pp. 266 – 295. DOI 10.1016/S0899-8256(03)00006-X
11. Winstrand M. (2004). Mission Planning and Control of Multiple UAV’s. Scientific Report. No. FOI-R-1382-SE Swedesh Defence Research Agency.
12. Zhu R., Sun D., Zhou Z. (2005). Cooperation Strategy of Unmanned Air Vehicles for Multitarget Interception. Journal Guidance, Vol. 28, (5), pp. 1068 – 1072. DOI 10.2514/1.14412
13. Chandler P. R., Pachter M., Rassmussen S. (2001). UAV Cooperative Control. Proceedings of the IEEE American Control Conference, Vol. 1, pp. 50 – 55. New York. DOI 10.1109/ACC.2001.945512
14. Takahashi O., Schilling R. (1989). Motion Planning in a Plane Using Voronoi Diagrams. IEEE Transaction on Robotics and Automation, Vol. 5, (2). DOI 10.1109/70.88035
15. Guibas L. J., Knuth D. E., Sharir M. (1992). Randomized Incremental Construction of Delanay and Voronoi Diagrams. Algoritmica, Vol. 7, (4), pp. 381 – 413.

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