Interaction between the attacking drones salvo and the anti-aircraft drones salvo as a computer antagonistic non-Newtonian 2D game

The paper considers problem of the attacking drone avoiding interception at the final stage of its flight. Duration of this stage is a few seconds. Drones are flying to the target, explode and die. The literature traditionally considers the attack and the anti-aircraft drones independently. It is proposed to identify the attacking and the anti-aircraft drones as a single oscillatory system with the antagonistic components. Antagonistic components are connected using the non-Newtonian elastic element. Test game with a high-explosive drone, test game with a fragmentation drone and 2D salvo game were considered. The game in this case is not a traditional minimax optimization problem, but appears to be simulation of the compromise unstable motion mode. Salvo of three attack drones in the 2D games is aimed against three stationary targets. Anti-aircraft salvo includes two high-explosive and two fragmentation drones. The attacking drones “know nothing” about the anti-aircraft target distribution; thus, each of them “avoids” the anti-aircraft drones simultaneously. One operator is playing. Therefore, the game has only two parameters, i.e. two different stiffness coefficients of any non-Newtonian elastic element. The non-Newtonian oscillatory system under study is non-oscillatory. There are violations of the well-known oscillation theorems of the oscillations theory: with the increasing rigidity, the system oscillation frequency drops, the oscillation forms acquire additional nodes, etc.

References

[1] Silnikov M.V., Lazorkin V.I. Active protection of mobile objects from the swarm of combat robots. Zashchita i bezopasnost, 2020, no. 4, pp. 18–19. (In Russ.).

[2] Silnikov M.V., Karpovich A.V., Lazorkin V.I. et al. Nauchno-metodicheskoe obosnovanie sposobov primeneniya letatelnykh apparatov dlya razvedki i porazheniya tseley [Methodological grounding for ways of using aircraft for surveillance and target acquisition]. Sankt-Petersburg, NPO Spetsialnykh materialov Publ., 2022, pp. 355–364. (In Russ.).

[3] Savin L.V. Future war: swarm of combat robots. Strategicheskaya stabilnost, 2017, no. 1, pp. 24–35. (In Russ.).

[4] Pronichev A.P., Chechulin A.A., Vitkova L.A. Approach to management of heterogeneous swarms of devices. Informatizatsiya i svyaz [Informatization and Communication], 2020, no. 5, pp. 119–124, doi: https://doi.org/10.34219/2078-8320-2020-11-5-119-124 (in Russ.).

[5] Nistyuk A.I., Turygin Yu.V., Khvorenkov V.V. et al. Algorithm for obtaining a characteristic polynom and transfer function of dynamic model of drone "swarm". Intellektualnye sistemy v proizvodstve [Intelligent Systems in Manufacturing], 2018, t. 16, no. 4, pp. 122–129, doi: https://doi.org/10.22213/2410-9304-2018-4-122-129 (in Russ.).

[6] Nistyuk A.I., Abilov A.V., Khvorenkov V.V. et al. Development of swarm technology for mobile self-organizing networks. Identification of dynamic models. Vestnik IzhGTU imeni M.T.D Kalashnikova, 2018, t. 21, no. 4, pp. 136–140, doi: https://doi.org/10.22213/2413-1172-2018-4-136-140 (in Russ.).

[7] Dovgal V.A., Dovgal D.V. Analysis of communication interaction systems for drones performing a search mission as part of a group. Vestnik AGU, 2020, no. 4. URL: http://vestnik.adygnet.ru/files/2020.4/6465/87-94.pdf (in Russ.).

[8] Vasilyev I.S., Kondranenkova T.E. [Study on application of neuron networks for control on drones swarm]. Sb. tr. konf. Problemy sovremennoy nauki i obshchestva: sokhranenie i razvitie naslediya velikoy Pobedy [Proc. Conf. Problems of Modern Science and Society: Preservation and Development of Great Victory Heritage]. Nizhniy Novgorod, NGIEI Publ., 2021, pp. 117–122. (In Russ.).

[9] Brusov V.S., Volkovoy A.V., Druzin S.V. et al. Bespilotnyy letatelnyy apparat-perekhvatchik [Unmanned aerial vehicle — interceptor]. Patent RU 2669904. Appl. 13.02.2018, publ. 16.10.2018. (In Russ.).

[10] Brusov V.S., Volkovoy A.V., Druzin S.V. et al. Bespilotnyy letatelnyy apparat-perekhvatchik [Unmanned interceptor]. Patent RU 2699148. Appl. 13.02.2018, publ. 03.09.2019. (In Russ.).

[11] Nassi B., Ben-Netanel R., Shamir A. et al. Game of drones - detecting streamed POI from encrypted FPV channel. arXiv:1801.03074, doi: https://doi.org/10.48550/arXiv.1801.03074

[12] Majd A., Loni M., Sahebi G. et al. Improving motion safety and efficiency of intelligent autonomous swarm of drones. Drones, 2020, vol. 4, no. 3, art. 48, doi: https://doi.org/10.3390/drones4030048

[13] Csengeri J. Counter-drone activity as a system. Security & Future, 2019, vol. 3, no. 1, pp. 31–34.

[14] Alsolami F., Alqurashi F.A., Hasan M.K. et al. Development of self-synchronized drones’ network using cluster-based swarm intelligence approach. IEEE Access, 2021, vol. 9, pp. 48010–48022, doi: https://doi.org/10.1109/ACCESS.2021.3064905

[15] Yasin J.N., Mohamed S.A.S., Haghbayan M.H. et al. Navigation of autonomous swarm of drones using translational coordinates. EasyChair Preprint 2020, no. 2971, 11 p.

[16] Park S., Kim H.T., Lee S. et al. Survey of anti-drone systems: components, design, and challenges. IEEE Access, 2021, vol. 9, pp. 42635–42659, doi: https://doi.org/10.1109/ACCESS.2021.3065926

[17] Dbouk T., Dikakis D. Quadcopter drones swarm aeroacoustics. Phys. Fluids, 2021, vol. 33, no. 5, art. 057112, doi: https://doi.org/10.1063/5.0052505

[18] Gantmakher F.R., Kreyn M.G. Ostsillyatsionnye matritsy i yadra i malye kolebaniya mekhanicheskikh system [Oscillating matrixes and kernels and small oscillations of mechanic systems]. Moscow, Gostekhizdat Publ., 1950. 360 p. (In Russ.).

[19] Gantmakher F.R. Teoriya matrits [Theory of matrixes]. Moscow, Nauka Publ., 1988. 548 p. (In Russ.).

[20] Babakov I.M. Teoriya kolebaniy [Theory of oscillations]. Moscow, Nauka Publ., 1968. 560 p. (In Russ.).

[21] Arinchev S.V. Teoriya kolebaniy nekonservativnykh system [Oscillation theory of non-conservative systems]. Moscow, Bauman MSTU Publ., 2002. 464 p. (In Russ.).