Method for constructing an aircraft route taking into account the terrain based on the integrated use of multi-agent algorithms
https://doi.org/10.17586/0021-3454-2024-67-5-395-405
Abstract
To determine a rational route for an aircraft, taking into account the terrain, a method for solving the corresponding problem is proposed based on the integrated use of multi-agent stochastic search algorithms. To assess the quality of the aircraft's route, taking into account the restrictions imposed on the problem, it is proposed to use a complex criterion in the form of a penalty function. An algorithm for generating a reference route option is developed based on the results of solving the problem using the method of river formation dynamics. A rational route for the movement of an aircraft is carried out using the particle swarm method. The data of the reference variant of the route of the aircraft are used to determine the values of the parameters of the algorithm of the particle swarm method and its initialization. Results of an experimental test are presented, demonstrating the performance and effectiveness of the described method.
About the Authors
O. V. Yesikov
Central Design Bureau of Apparatus Engineering
Russian Federation
Russian Federation
Oleg V. Yesikov - Dr. Sci., Professor; Deputy Head of the Department
Tula
D. O. Yesikov
VimpelСom–Information Technologies LLC
Russian Federation
Russian Federation
Dmitry O. Yesikov - PhD; Senior Architect
Tula
A. V. Danilov
Branch of the Military Academy of Logistics
Russian Federation
Russian Federation
Alexander V. Danilov - Branch of the Military Academy of Logistics; Head of the Department
Penza
M. S. Zemlyanitsyn
Tula State University
Russian Federation
Russian Federation
Maksim S. Zemlyanitsyn - Student
Tula
References
1. Moiseev V. S. Dinamika poleta i upravleniye bespilotnymi letatel'nymi apparatami (Flight Dynamics and Control of Unmanned Aerial Vehicles), Kazan, 2017, 416 р. (in Russ.)
2. Esikov O. V., Akinshin O. N., Khomyakov K. A., Agafonov D. O. Modeli i algoritmy imitatsii dvizheniya vozdushnykh tseley v trenazhernykh kompleksakh (Models and Algorithms for Simulating the Movement of Air Targets in Training Complexes), Tula, 2022, 115 р. (in Russ.)
3. Shpenst V. A., Morozova O. Yu., Beloshitsky A. A. Journal of Instrument Engineering, 2021, no. 6, pp. 503–508.(in Russ.)
4. Vasilchenko A. S., Ivanov M. S., Kolmykov G. N. Systems of Control, Communication and Security, 2019, no. 4, pp. 403–420, DOI: 10.24411/2410-9916-2019-10416. (in Russ.)
5. Bortakovskii A., Uryupin I. Trudy MAI, 2020, no. 113, DOI: 10.34759/trd-2020-113-17. (in Russ.)
6. Kozub A. N., Kucherov D. P. Sistemy i sredstva iskusstvennogo intellekta, 2013, no. 4, pp. 333–343 (in Russ.)
7. Marusin V. S., Ponomarev O. P., Stolyarov O. G., Temerov O. P. Vestnik Kontserna VKO „Almaz–Antey“, 2019, no. 1, pp. 98–104. (in Russ.)
8. Yakovlev K. S., Baskin E. S., Andreychuk A. A. Management of Large Systems, 2015, no. 58, pp. 306–342. (in Russ.)
9. Alilueva N. V., Rudenko E. M. I-methods, 2018, no. 1(10), pp. 5–18. (in Russ.)
10. Zhuk A. A., Buloichyk V. M., Akulich S. V. System Analysis and Appliied Information Science, 2022, no. 3, pp. 43–49. (in Russ.)
11. Filimonov A. B., Filimonov N. B., Nguyen T. K., Pham Q. P. Мechatronics, Automation, Control, 2023, no. 7(24), pp. 374–381. (in Russ.)
12. Belokon’ S. A., Zolotukhin Y. N., Nesterov A. A. Optoelectronics, Instrumentation and Data Processing, 2017, no. 1(53), pp. 3–8.
13. Khachumov M. V. Artificial Intelligence and Decision Making, 2018, no. 1, pp. 3–11. (in Russ.)
14. Yesikov O. V., Danilov A. V., Zemlyanitsyn M. S. News of the Tula State University. Technical Sciences, 2022, no. 12, pp. 156–159. (in Russ.)
15. Yesikov O. V., Akinshin R. N., Danilov A. V., Zemlyanitsyn M. S. Elektronnyye informatsionnyye sistemy, 2023, no. 1(36), pp. 17–26. (in Russ.)
16. Karpenko A. P. Sovremennyye algoritmy poiskovoy optimizatsii. Algoritmy, vdokhnovlennyye prirodoy (Modern Search Engine Optimization Algorithms. Algorithms Inspired by Nature), Moscow, 2017, 446 р. (in Russ.)
17. Nikolos I. K., Valavanis K. P., Tsourveloudis N. C., Kostaras A. N. IEEE Transactions on systems, man, and cybernetics — part b: cybernetics, 2003, DOI: 10.1109/TSMCB.2002.804370.
18. Gladkov L. A., Kureichik V. V., Kureichik V. M. Geneticheskiye algoritmy (Genetic Algorithms), Moscow, 2006, 320 р. (in Russ.)
19. Eberhart R. C., Kennedy J. Proc. IEEE Intern. Conf. on Neural Networks, Piscataway, IEEE Service Center, 1995, no. 4, pp. 1942–1948.
20. Clerc M. Particle swarm optimization, NJ, Wiley-Interscience, 2006, 243 p.
21. Yesikov D. O., Ivutin A. N. 5th Mediterranean Conference on Embedded Computing (MECO), June 12–16, 2016, рр. 309–312, DOI: 10.1109/MECO.2016.7525769.
22. Leguizamon G., Blum C., Alba E. Handbook of approximation algorithms and metaheuristics, Boca Raton, CRC press, 2007, рр. 372–386.
23. Rabanal P., Rodriguez I. and Rubio F. Nature-Inspired Algorithms for Optimisation, vol. 193. Studies in Computational Intelligence, Springer, Berlin Heidelberg, 2009, рр. 333–368.
24. Yesikov O. V., Yesikov D. O., Akinshina N. Yu. Instruments and Systems: Monitoring, Control, and Diagnostics, 2018, no. 5, pp. 47–56. (in Russ.)
25. Yesikov O., Yesikov D., Rumiantsev V., Ivutin A. 8th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, June 10–15, 2019, DOI:10.1109/MECO.2019.8760067.
Review
For citations:
Yesikov O.V., Yesikov D.O., Danilov A.V., Zemlyanitsyn M.S. Method for constructing an aircraft route taking into account the terrain based on the integrated use of multi-agent algorithms. Journal of Instrument Engineering. 2024;67(5):395-405. (In Russ.) https://doi.org/10.17586/0021-3454-2024-67-5-395-405
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