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Method and Algorithm of Autonomous Flight Trajectory Planning of an Unmanned Aerial Vehicle When Monitoring the Fire Situation in Order to Detect the Source of Ignition Early

https://doi.org/10.21869/2223-1536-2022-12-4-64-85

Abstract

The purpose of the research is to develop a method and algorithm for autonomous planning of the flight path of an unmanned aerial vehicle when monitoring the fire situation, which are designed for early detection of the source of ignition. Since timely detection of a fire source at the stage of its development allows to reduce both material and human losses, the development of a method and algorithm for autonomous flight trajectory planning of an unmanned aerial vehicle when monitoring the fire situation in order to detect the source of ignition early is an urgent, important task.

Methods. A method for detecting the source of ignition is proposed. The method is based on three flight plans. Plan A provides for a flyby of the monitored area by gas with determination of the concentration of a harmful substance in each pixel of the monitoring area. When a pixel is detected in which the concentration of a harmful substance exceeds the threshold level, the flight control of the UAV is carried out through plan B, which provides for local planning to achieve the pixel target, which is determined by calculating local differential operators in a nine–element mask. Plan B allows the UAV to fly directly to the source of the fire and determine its coordinates. Flight plan C provides for the return of the UAV to the point of departure from any pixel of the monitoring zone.

Results. An algorithm for controlling the UAV flight path has been developed, which allows determining local target pixels and building a local flight plan on this basis. The basis for constructing a local flight plan is the rule "at least three pixels on the tack", which allows you to obtain a nine-element matrix with known concentrations of harmful substances in the target pixel and determine those pixels of the local flight plan in which it is not possible to obtain this matrix.

Conclusion. Mathematical modeling of the UAV flight control algorithm according to the proposed test method was implemented in the Matlab 8.0 package and showed control stability and high speed of reaching the coordinates of the pixel of the ignition source, exceeding the speed of achieving the set goal by 1.5...2 times, depending on the location of the ignition source relative to the direction of the flyby of the monitored territory. 

About the Authors

R. A. Tomakova
Southwest State University
Russian Federation

Rimma A. Tomakova, Dr. of Sci. (Engineering), Professor, 

50 Let Oktyabrya Str. 94, Kursk 305040



S. A. Filist
Southwest State University
Russian Federation

Sergey А. Filist, Dr. of Sci. (Engineering), Professor, Professor of the Department of Biomedical Engineering,

50 Let Oktyabrya Str. 94, Kursk 305040



A. N. Brezhneva
Plekhanov Russian University of Economics
Russian Federation

Aleksandra N. Brezhneva, Cand. of Sci. (Engineering), Associate Professor of the Department of Informatics,

36 Stremyanny side-street, Moscow 117997



I. N. Gorbachev
Southwest State University
Russian Federation

Igor N. Gorbachev, Post-Graduate Student, 

50 Let Oktyabrya Str. 94, Kursk 305040



Ya. O. Zaikin
Southwest State University
Russian Federation

Yaroslav O. Zaikin, Post-Graduate Student, 

50 Let Oktyabrya Str. 94, Kursk 305040



References

1. Lesnye pozhary v Rossii [Forest fires in Russia]. Available at: https://tass.ru/info/15559017. (accessed 12.02.2023)

2. Safonova N. L., Drobushko A. G. GIS-tekhnologii dlya prognozirovaniya lesnykh pozharov [GIS-technologies for forecasting forest fires]. Problemy obespecheniya bezopasnosti pri likvidatsii posledstvii chrezvychainykh situatsii = Problems of Ensuring Safety During Liquidation of Consequences of Emergency Situations, 2016, no. 1-2 (5), pp. 38–40.

3. Pozhary i pozharnaya bezopasnost' v 2015 godu. Statisticheskii sbornik [Fires and fire safety in 2015. Statistical collection]; ed. by A. V. Matyushina. Moscow, All-Russian Order "Badge of Honor" Scientific Research Institute of Fire Defense Publ., 2016. 124 p.

4. Pozhary i pozharnaya bezopasnost' v 2021 godu. Statisticheskii sbornik [Fires and fire safety in 2021. Statistical collection]. Balashikha, All-Russian Order "Badge of Honor" Scientific Research Institute of Fire Defense Publ., 2022. 114 p.

5. Soveshchanie o likvidatsii prirodnykh pozharov [Meeting on the elimination of wildfires]. Available at: http://www.kremlin.ru/catalog/persons/693/events/69202. (accessed 18.01.2023)

6. Favorskaya M., Pyataeva A., Popov A. Spatio-temporal smoke clustering in out-door scenes based on boosted random forests. Procedia Computer Science, 2016, vol. 96, pp. 762– 771.

7. Foggia P., Saggese A., Vento M. Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion. IEEE Transactions on Circuits and Systems for Video Technology, 2015, vol. 25, no. 9, pp. 1545–1556.

8. Tomakov M. V., Tomakov V. I. Profilaktika lesnykh pozharov na territorii Kurskoi oblasti. Bezopasnost' zhiznedeyatel'nosti = Life Safety, 2020, no. 4 (232), pp. 52–60.

9. Ivanov V. P., Marchenko S. I., Nartov D. I. Protivopozharnaya profilaktika lesnykh ob"ektov [Fire prevention of forest objects]. Lesnoi zhurnal. Izvestiya vysshikh uchebnykh zavedenii = Forest Journal. News of Higher Educational Institutions, 2019, no. 3, pp. 43–54. https://doi.org/10.17238/issn0536-1036.2019.3.43

10. Bykovskii V. K. Okhrana lesov ot pozharov v Rossii i stranakh SNG [Protection of forests from fires in Russia and CIS countries]. Mezhdunarodnoe sotrudnichestvo evraziiskikh gosudarstv: politika, ekonomika, pravo = International Cooperation of Eurasian States: Politics, Economics, Law, 2016, no. 3, pp. 105–110.

11. Burlachenko K. G., Efimov A. A., Kadiev Sh. K., eds. Perspektivnye napravleniya nauchno-tekhnicheskoi deyatel'nosti MChS Rossii v oblasti informatsionnykh tekhnologii [Promising directions of scientific and technical activity of the Ministry of Emergency Situations of Russia in the field of information technologies]. Vestnik Universiteta grazhdanskoi zashchity MChS Belarusi = Bulletin of the University of Civil Protection of the Ministry of Emergency Situations of Belarus, 2022, vol. 6, no. 4, pp. 481–497.

12. Shevtsov M. V., Aksenov V. V., Safronov R. I., Shul'ga L. V., Degtyarev S. V. Mobil'naya sistema monitoringa, rannego obnaruzheniya i otsenki pozharnoi opasnosti [Mobile monitoring system, early detection and assessment of fire danger]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2021, vol. 11, no. 3, pp. 8–25.

13. Tomakova R. A., Emel'yanov S. G., Filist S. A. Intellektual'nye tekhnologii segmentatsii i klassifikatsii biomeditsinskikh izobrazhenii [Intelligent technologies of segmentation and classification of biomedical images]. Kursk, Southwest State University Publ., 2012. 222 p.

14. Dyudin M. V., Zuev I. V., Chudinov S. M., eds. Avtomaticheskie klassifikatory slozhnostrukturiruemykh izobrazhenii na osnove mul'timetodnykh tekhnologii mnogokriterial'nogo vybora [Automatic classifiers of complex structured images based on multimethod technologies of multicriteria selection]. Voprosy radioelektroniki. Seriya: Sistemy i sredstva otobrazheniya informatsii i upravleniya spetstekhnikoi (SOIU) = Radio Electronics Issues. Series: Systems and Means of Information Display and Control of Special Equipment (SOIU), 2015, is. 1, pp. 130–141.

15. Kassim K. D. A., Kuz'min A. A., Shatalova O. V., eds. Formirovanie priznakovogo prostranstva dlya zadach klassifikatsii slozhnostrukturiruemykh izobrazhenii na osnove spektral'nykh okon i neirosetevykh struktur [Formation of a feature space for classification problems of complex structured images based on spectral windows and neural network structures]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University, 2016, no. 4 (67), pp. 56–68.

16. Tomakova R. A., Filist S. A., Durakov I. V. Programmnoe obespechenie avtomaticheskoi klassifikatsii rentgenogramm grudnoi kletki na osnove gibridnykh klassifikatorov [Software for automatic classification of chest radiographs based on hybrid classifiers]. Ekologiya cheloveka = Human Ecology, 2018, no. 6, pp. 59–64.

17. Filist S. A., Shatalova O. V., Efremov M. A. Gibridnaya neironnaya set' s makrosloyami dlya meditsinskikh prilozhenii [Hybrid neural network with macro layers for medical applications] Neirokomp'yutery. Razrabotka, primenenie = Neurocomputers. Development, Application, 2014, no. 6, pp. 35–39.

18. Shatalova O. V., Kuz'min A. A., Kassim K. D. A., eds. Metod klassifikatsii slozhnostrukturiruemykh izobrazhenii na osnove sa-moorganizuyushchikhsya neirosetevykh struktur [Method of classification of complex structured images based on self-organizing neural network structures]. Radiopromyshlennost' = Radio Industry, 2016, no. 4, pp. 57–65.

19. Korenevskii N. A., Shevtsov M. V., Starodubtseva L. V., Siplivyi G. V. Metod sinteza matematicheskikh modelei otsenki pozharnoi obstanovki i sostoyaniya lyudei, nakhodyashchikhsya v zone pozhara [Method of synthesis of mathematical models for assessing the fire situation and the condition of people in the fire zone]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2021, vol. 11, no. 3. pp. 142–159.

20. Filist S. A., Tomakova R. A., Nefedov N. G., Puzyrev E. I., Gorbachev I. N. Intellektual'naya sistema obrabotki izobrazhenii, poluchaemykh s bespilotnykh letatel'nykh apparatov [Intelligent image processing system obtained from unmanned aerial vehicles]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2022, vol. 12, no. 4, pp. 64-86.

21. Titov D. V., Emel'yanov S. G., Trufanov M. I. Bystrodeistvuyushchaya sistema tekhnicheskogo zreniya dlya poiska i opredeleniya kharakteristik ochaga vozgoraniya [Highspeed vision system for searching and determining the characteristics of a fire source]. Izvestiya vysshikh uchebnykh zavedenii. Priborostroenie = News of Higher Educational Institutions. Instrumentation, 2012, no. 2, pp. 40–43.

22. Filist S. A., Shevtsov M. V., Belozerov V. A., eds. Avtomatizirovannaya sistema dlya klassifikatsii snimkov videopotokov [Automated system for classifying images of video streams]. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering, 2021, vol. 11, no. 4, pp. 85–105.


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Tomakova R.A., Filist S.A., Brezhneva A.N., Gorbachev I.N., Zaikin Ya.O. Method and Algorithm of Autonomous Flight Trajectory Planning of an Unmanned Aerial Vehicle When Monitoring the Fire Situation in Order to Detect the Source of Ignition Early. Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering. 2023;13(1):93-110. (In Russ.) https://doi.org/10.21869/2223-1536-2022-12-4-64-85

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