CONEPT OF A ROBOT GROUP CALCULATION
Abstract
The problem of calculation of an autonomous robotic group in order to destroy the detected enemy group is considered. A group of robots must be formed in such a way that the task assigned to it to destroy the enemy group is performed with a high degree of probability. The task is solved as an assignment problem. The initial information for solving this problem are types and numberof objects of the detected enemy group, positions of the enemy objects, information about the war possibilities of the tools available in our group, the type of group being formed (robotic or mixed), the purpose of the operation, the actions of the group at the end of the operation. We propose a solution to the problem based on the evaluation of the effectiveness of individual robotic systems. The solution is formulated as a sequence of the four stages. At the first stage, the calculation of a priori effectiveness of each element of the detected enemy group is performed. At the second stage, based on expert assessments, the choice of efficiency coefficients for each of the available robotic systems against each element of the detected enemy group is made. At the third stage, a priori estimates of the effectiveness of the available robotic systems are corrected, taking into account the coefficients selected at the second stage. At the fourth stage, a group of robotic systems is formed in such a way that its total application efficiency exceeds the total application efficiency of the detected enemy by 2.0–2.5 times. The proposed method of forming a group allows you to cre-ate both quantitative and qualitative composition of the group. The article provides an example of the formation of a group whose goal is to neutralize an exposed enemy.
References
2. Dul’nev P.A., Pedenko N.P., Starovoytov S.N., Sychov S.A. K voprosu razvitiya robototekhnicheskikh sredstv sukhoputnykh voysk i otsenki effektivnosti ikh boevogo primeneniya [On the issue of development of robotic means of the land forces and evaluation of the effectiveness of their combat use]. Voennaya mysl' [Military thought], 2019, No. 7, pp. 147-156.
3. Rubtsov I.V. Voprosy sostoyaniya i perspektivy razvitiya otechestvennoy nazemnoy robototekhniki voennogo i spetsial'nogo naznacheniya [Current situation and perspective of devel-opment for ground military and special robotics]. Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFEDU. Engineering Scinces], 2013, No. 3 (140), pp. 23-29.
4. Rizk Y., Awad M., Tunstel E.W. Cooperative heterogeneous multi-robot systems: A survey, ACM Computing Surveys Volume, 2019, Vol. 52 (2).
5. Xu B., Yang Z., Ge Yu., and Peng Z. Coalition Formation in Multi-agent Systems Based on Improved Particle Swarm Optimization Algorithm, International Journal of Hybrid Infor-mation Technology, 2015, Vol. 8 (3), pp. 1-8.
6. Tan Y., Zheng Z.-Y. Research Advance in Swarm Robotics. Defense Technology, 2013, Vol. 9 (1), pp. 18-39.
7. Grigin N.V. Organizatsiya sistemy zakupok vooruzheniya i voennoy tekhniki dlya ministerstv oborony vedushchikh stran NATO [Organization of the procurement system for weapons and military equipment for the Ministry of defense of the leading NATO countries]. FGUP Trudy Krylovskogo gosudarstvennogo nauchnogo centra [Proceedings of the Krylov state scientific center], 2017, No. 380, pp. 148-160.
8. Boevoy robot «Ural-9» [Ural-9 combat robot], 2017. Available at: http://huntsmanblog.ru/ boevoj-robot-ural-9/ (accessed 20 February 2020).
9. Boevoy robotizirovannyy kompleks "Nerekhta" [Nerekhta combat robot]. Elektronnyy resurs [Electronic resource]. Novosti VPK [News of the military -industrial complex], 2019. Availa-ble at: https://vpk.name/library/f/nerehta-rtk.html (accessed 20 February 2020).
10. BAS-01G BM "Soratnik", variant № 2. Armiya-2016 [BAS-01G BM "Soratnik", variant 2, Army-2016], 2016. Available at: https://yuripasholok.livejournal.com/9424148.html (accessed 20 February 2020).
11. Zubov V.N. Novye rossiyskie voennye robototekhnicheskie kompleksy [New Russian military robotic systems], Voprosy oboronnoy tekhniki. Seriya 16: Tekhnicheskie sredstva protivodeystviya terrorizmu [Questions of defense equipment. Series 16: Technical means of countering terrorism], 2017, No. 5-6 (107-108), pp. 73-81. ISSN: 2306-1456.
12. Parshin N.M., Stepanov O.A., Kurenkov N.I., Ananjev S.N. Kontseptual'nyy podkhod k otsenke effektivnosti primeneniya sistemy vysokotochnogo oruzhiya v operatsiyakh [A conceptual ap-proach to evaluating the effectiveness of a precision weapon system in operations]. Voennaya mysl' [Military thought], 2019, No. 3, pp. 72-81.
13. Kostin N.A. Metodicheskiy podkhod k opredeleniyu boevykh potentsialov voyskovykh formirovaniy [Methodological approach to determining the combat potential of military for-mations]. Voennaya mysl' [Military thought], 2017, No. 10, pp. 33-48.
14. Brezgin V.S. Metodika rascheta boevykh potentsialov obrazcov vooruzheniya i voennoy tekhniki po rezul'tatam imitatsionnogo modelirovaniya boevykh deystviy [Method of calculating the combat po-tential of weapons and military equipment samples based on the results of simulation of combat op-erations]. Vooruzhenie i ekonomika [Armament and economy], 2009, No. 1 (5), pp. 30-34.
15. Leonov A.V., Trushenkov V.V., Nesterov D.V. Algoritm voenno-ekonomicheskoy otsenki effektivnosti ispol'zovaniya robototekhnicheskikh kompleksov v sostave podrazdeleniy Vooruzhennykh Sil Rossiyskoy Federatsii [Algorithm for military -economic evaluation of the effectiveness of using robotic systems in the armed Forces of the Russian Federation]. Voennaya mysl' [Military thought], 2019, No. 7, pp. 81-90.
16. Burenok V.M., Pogrebnyak R.N., Skotnikov A.P. Metodologiya obosnovaniya perspektiv razvitiya sredstv vooruzhennoy bor'by obshchego naznacheniya [Methodology for substantiat-ing the prospects for the development of General-purpose weapons]. Ed. by V.V. Panov. Mos-cow: Mashinostroenie, 2010, 368 p.
17. Gaiduk A.R., Karkischenko A.N., Pshikhopov V.Kh. O vliyanii RTK VN na effektivnost' ispol'zovaniya VVT [About influence of the military rtk on efficiency use of WMT], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFEDU. Engineering Scinces], 2019, No. 1 (203), pp. 61-74.
18. Gaiduk A.R. Polinomial design of the stochastic optimal, minimal complication system, Lec-ture Notes in Control and Information Sciences, 1990, pp. 611-615.
19. Kalyaev I.A., Gaiduk A.R., Kapustyan S.G. Modeli i algoritmy kollektivnogo upravleniya v gruppakh robotov [Models and algorithms for collective control in robot groups]. Moscow: Fizmatlit, 2009, 280 p.
20. Gaiduk A.R., Kapustyan S.G. Kontseptsiya postroeniya sistem kollektivnogo upravleniya bespilotnymi letatel'nymi apparatami [The concept of building collective control systems for unmanned aerial vehicles], In book: Sistemy radioupravleniya. Kn. 4. Optimizaciya algoritmov upravleniya [Radio control systems. Book 4. Optimization of control algorithms]. Ed. by V.I. Merkulov. Moscow: Radiotekhnika, 2018, pp. 39-46.
21. Geng M., Xu K., Zhou X., Ding B., Wang H., Zhang L. Learning to cooperate via an attention-based communication neural network in decentralized multi-robot exploration, Entropy, 2019, Vol. 21 (3).
22. Martínez-García E.A., Torres-Córdoba R., Carrillo-Saucedo V.M., López-González E. Neural control and coordination of decentralized transportation robots, Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 2018, Vol. 232 (5), pp. 519-540.
