2021, issue 2, p. 25-38
Received 03.03.2021; Revised 18.03.2021; Accepted 24.06.2021
Published 30.06.2021; First Online 01.07.2021
https://doi.org/10.34229/2707-451X.21.2.3
Previous | Full text (in Ukrainian) | Next
Statement of the Problem of Complete Set of UAV Group on the Basis of Models of Granular Calculations and Fuzzy Logic
Vyacheslav
Korolyov 
,
Maksim Ogurtsov ,
Alexander Khodzinsky *
V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv
* Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.
Introduction. The increase in the number of heterogeneous groups of UAVs that jointly perform aerial photography missions generates a large amount of poorly structured information: videos, photos, telemetry records, navigation data. To build intelligent databases from unstructured information sources from UAV groups, granular computational approaches are proposed. These approaches are the basis for the application of Big Data technologies and artificial intelligence to increase situational awareness or commercial value of knowledge gained from the data flow from UAV groups.
The purpose of the article. Develop new models for assessing the quality of video data from UAVs, approaches to equipping heterogeneous groups of UAVs and indicators for assessing its tactical and technical characteristics as a team.
Results. The success of UAV group mission planning is based on the forecast of quantitative and qualitative indicators of the received video data. For this purpose, a model for forecasting the quality of the obtained aerial photographs based on the data on the speed, height of the UAV and the angle of the video camera is proposed. The model is based on the development of the theory of fuzzy sets of the first and second types. An example of the implementation of the model in the system of computer mathematics MatLab 2020b is given.
Based on the analysis of a number of works on UAV classification and the proposed model of image quality, the method of equipment for the UAV group and the choice of UAV types are built, as well as the content of the combinatorial optimization problem based on the classic backpack problem. An example of calculations of tactical and technical characteristics for the Ukrainian UAV "Spectator" of Meridian ltd. is given.
Conclusions. A new model for assessing the quality of aerial photography images based on fuzzy logic has been developed. The method of staffing UAV groups is proposed.
Keywords: Fuzzy logic, granular calculations, UAV equipment, heterogeneous groups, computer simulation.
Cite as: Korolyov V., Ogurtsov M., Khodzinsky A. Statement of the Problem of Complete Set of UAV Group on the Basis of Models of Granular Calculations and Fuzzy Logic. Cybernetics and Computer Technologies. 2021. 2. P. 25–38. (in Ukrainian) https://doi.org/10.34229/2707-451X.21.2.3
References
1. Korolyov V.Yu. Reusable cruise missile routing. Control systems and machines. 2019. 2. P. 16–24. https://doi.org/10.15407/usim.2019.02.016
2. Korolyov V.Yu., Khodzinsky O.M. Topological-combinatorial model of network construction for vehicles. Computer mathematics. 2018. 1. P. 61–67. http://dspace.nbuv.gov.ua/handle/123456789/161850
3. Korolyov V.Yu., Ogurtsov M.I., Khodzinsky O.M. On the task of constructing traffic routes for a group of mobile remotely controlled systems. Computational intelligence. Labor International. scientific-practical conf., 16 – 18.05.2017. Kyiv: "Kyiv University". 2017. P. 247–248.
4. Korolyov V.Yu., Polinovsky V.V., Ogurtsov M.I. Modeling of communication networks of mobile remotely controlled systems based on HLA. Bulletin of Khmelnytsky National University. 2017. 1 (245). P. 160–165. http://nbuv.gov.ua/j-pdf/Vchnu_tekh_2017_1_33.pdf
5. Korolyov V.Yu., Ogurtsov M.I. Transport and communication task for groups of unmanned aerial vehicles. Mathematical machines and systems. 2017. 1. Р. 82–89. http://dspace.nbuv.gov.ua/handle/123456789/117508
6. Hulyanitskyi L.F., Korolyov V.Yu., Ogurtsov M.I., Khodzinsky O.M. The problem of UAV group routing in search and monitoring tasks. Computer mathematics. 2018. 2. P. 38–47. http://dspace.nbuv.gov.ua/handle/123456789/161884
7. Korolyov V.Yu., Ogurtsov M.I., Khodzinsky O.M. Mathematical modeling of routes of mobile remotely controlled systems and their groups during the survey of the territory. Intelligent decision-making systems and problems of computational intelligence. International materials. Science. conf. Kherson: Publishing house P.P. Vyshemirsky 2017. P. 199–201.
8. Korchenko A.G., Ilyash O.S. Generalized classification of unmanned aerial vehicles. Collection of scientific works of Kharkiv University of the Air Force. 2012. 4. P. 27–36.
9. Ponda S.S., Johnson L.B., Geramifard A., How J.P. Cooperative mission planning for multi-UAV teams. Handbook of Unmanned Aerial Vehicles. Dordrecht: Springer. 2015. P. 1447–1490. https://doi.org/10.1007/978-90-481-9707-1_16
10. Ogurtsov M.I. Review of the task of determining the composition of the UAV team, necessary to perform the task. International. Science. symposium "INTELLECTUAL SOLUTIONS". Computational intelligence: international works. scientific-practical conf. 04/15/2019 Uzhhorod: Uzhhorod National University. 2019. P. 117–118.
11. Hulyanytskyi L.F., Ogurtsov M.I. Determining the optimal composition of the UAV group to perform the task based on the theory of fuzzy sets. Information technology and computer modeling. Materials of articles International. scientific-practical conf. Ivano-Frankivsk, 18-22.05.2020. Ivano-Frankivsk: Mr. Goliney O.M. 2020. P. 36–39.
12. Khodzinsky O.M., Ogurtsov M.I. Development of a unified evaluation system for digital devices. Bulletin of the University "Ukraine". Series "Informatics, Computing and Cybernetics". 2010. 8. P. 145–149.
13. Leonenkov A.V. Fuzzy modeling in MATLAB and FuzzyTECH. Petersburg: BHV-Petersburg. 2005. 736 p.
14. Kononov V.B., Kushneruk Y.I., Koval A.V. A method for solving the problem of replacing the armament of a group of troops. Weapons systems and military equipment. 2012. 2 (30) P. 162–166.
15. FM 3-04.155 Army Unmanned Aircraft System Operations. Headquarters. Department of Army. Washington. 2009. 152 p.
16. Koch W.L. Shadow Troop Handbook. A doctrinal approach to the employment of the RQ7-B Shadow Tactical Unmanned Aircraft System in the Armed Reconnaissance Squadron. Combat Aviation Brigade, 1st Infantry Division. 2011. P. 57.
17. Pedrycz W., Chen Sh.-M. Granular Computing and Intelligent Systems. Design with Information Gran-ules of Higher Order and Higher Type. Springer. 2011. P. 306.
18. Pedrycz W., Chen Sh.-M. Information Granularity, Big Data, and Computational Intelligence. Springer. 2015. P. 444. https://doi.org/10.1007/978-3-319-08254-7
19. Yao J.-T. Novel Development in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation. Information Science Reference. New York, 2010. P. 570. https://doi.org/10.4018/978-1-60566-324-1
20. Bell R., Falcon R., Pedrycz W., Kacprzyk J. Granular Computing: At the Junction of Rough Sets and Fuzzy Sets. Springer. 2008. 352 p. https://doi.org/10.1007/978-3-540-76973-6
21. Koul A., Ganju S., Kasam M. Practical Deep Learning for Cloud, Mobile, and Edge. O’Reilly. 2020, Р. 620.
22. Borisov A.N., Krumberg O.A., Fedorov I.P. Decision making based on fuzzy models: examples of use. Riga: Know-ledge. 1990. 184 p.
23. Bpak Spectator. https://merydian.kiev.ua/services/bezpilotnuy-avia-kompleks/ (accessed 12.11.2021)
ISSN 2707-451X (Online)
ISSN 2707-4501 (Print)
Previous | Full text (in Ukrainian) | Next