The concept of a new generation virtual poligon based on multiagent technologies for verifying the software of the robotic complexes with a high degree of autonomy

 Cover v7 iss3 2019

The concept of a new generation virtual poligon based on multiagent technologies for verifying the software of the robotic complexes with a high degree of autonomy

Sergey G. Tsarichenko
Doctor of Technical Science, Ministry of Defence of the Russian Federation, Main Robotics Research and Test Center, Senior Research Scientist, 5, Seregina ul., Moscow, 125167, Russia, tel.: +7(903)722-61-94, This email address is being protected from spambots. You need JavaScript enabled to view it.  

Evgeny V. Postnikov
Doctor of Technical Science, Saint Petersburg Electrotechnical University «LETI», Professor, 5, ul. Professora Popova, Saint-Petersburg, 197376, Russia, tel.: +7(962)680-15-79, This email address is being protected from spambots. You need JavaScript enabled to view it.  

Mikhail G. Panteleev *
PhD in Technical Sciences, Saint Petersburg Electrotechnical University «LETI», Assistant Professor, 5, ul. Professora Popova, Saint-Petersburg, 197376, Russia, tel.: +7(812)313-78-09, This email address is being protected from spambots. You need JavaScript enabled to view it.  


Received 06 May 2019

Abstract
The aim of the study is to develop the concept of the new generation virtual poligon for testing promising independently operated robotic systems (IORS). The need for the use of simulation modeling (SM) when testing IORS is due to key trends in the development of this class of sys-tems and the inability to conduct their full-scale tests in a variety of required conditions, for example, when countering robotic groups. One of the important problems is to check the firmware (software) of such systems in a wide range of conditions and scenarios of target application. The creation of an appropriate technological platform, along with the development of simulation environment and simulation tools for robots' test prototypes, also involves the development of a number of important methodological and organizational issues. The stages of the development of simulation models and their integration for testing in conjunction with the life cycle of the products created are highlighted. The possibility of independent development of simulation models of various IORS, their components and subsystems by various enterprises with relevant knowledge and subsequent integration of these models within a single simulation environment is important. The basic requirements for the simulation environment are formulated, the most important of which are versatility, openness and scalability. Taking into account these requirements, it is advisable to use the distributed simulation architecture – HLA (High Level Architecture) as a basic technology for creating a virtual polygon. At the same time, to test the onboard software of this class of systems, the key requirement is to support the real-time mode. Ensuring a high degree of autonomy involves the implementation of fulling the J. Boyd's cycle including intelligent subsystems for situational awareness and tactical planning onboard IORS. IORS are considered as intelligent real-time agents, and simulation of robotic groups is considered from the standpoint of modeling multi-agent systems. The architecture of a hybrid simulation environment for testing the IORS onboard software directly on the target platforms is proposed. The environment provides the ability to integrate simulation models of the physical objects (including models of physical robot platforms) with real onboard software and hardware platforms of IORS of different manufacturers.

Key words
Virtual polygon; robotic complexes with a high degree of autonomy; simulation modeling; HLA, High Level Architecture, multi-agent system; simulation environment architecture.

DOI

https://doi.org/10.31776/RTCJ.7301  

Bibliographic description
Tsarichenko, S., Postnikov, E. and Panteleev, M. (2019). The concept of a new generation virtual poligon based on multiagent technologies for verifying the software of the robotic complexes with a high degree of autonomy. Robotics and Technical Cybernetics, 7(3), pp.165-175.

UDC identifier:
004.453

References

  1. U.S. Government, Department of Defense, U.S. Military (2014). Unmanned Systems Integrated Roadmap FY2013-2038. Progressive Management. 168 p.
  2. Lopota, A. and Nikolaev, A. Nazemnye robototekhnicheskie kompleksy voennogo i spet-sial'nogo naznacheniya [Ground robotic systems for military and special purposes]. GNC RF CNII robototehniki i tehnicheskoj kibernetiki Publ. 30 p. (in Russian).
  3. Boulanin, V. and Verbruggen, M. (2017). Mapping the Development of Autonomy in Weapon Systems. Sweden: Stockholm International Peace Research Institute (SIPRI) Publ. 147 p.
  4. Feickert, A., Kapp, L., Elsea, J. and Harris, L. (2018). U.S. Ground Forces Robotics and Autonomous Systems (RAS) and Artificial Intelligence (AI): Considerations for Congress. Con-gressional Research Service Report. Washington D.C. Library of Congress. Congressional Research Service. 47 p.
  5. Cummings, M. (2017). Artificial Intelligence and the Future of Warfare. International Security Department and US and the Americas Programme. 18 p.
  6. Sheremet, I., Sheremet, I. and Ishchuk, V. (2014). K voprosu o sistemnoi otsenke effek-tivnosti robototekhnicheskikh kompleksov voennogo naznacheniya s ispol'zovaniem innovatsionnykh tekhnologii na baze modelirovaniya voennykh deistvii [On the issue of system evaluation of the effectiveness of robotic systems for military purposes using innovative technologies based on the simulation of military operations]. Oboronnyi kompleks nauchno-tekhnicheskomu progressu Rossii, 4(124), pp.21-26. (in Russian).
  7. Davis, P. (2003). Military Applications of Simulation. Applied System Simulation: Methodologies and Applications, Ed. by M.S. Obaidat, G.I. Papadimitriou: Kluwer Academic Publishers Publ., pp.407-435.
  8. Cila, I. and Mala, M. (2010). A multi-agent architecture for modelling and simulation of small military unit combat in asymmetric warfare. Expert Systems with Applications, 37(2), pp.1331-1343.
  9. Puzankov, D. and Panteleev, M. (2015). Intellektual'nye agenty i mnogoagentnye sistemy [Intelligent agents and multi-agent systems]. Saint Petersburg: SPbEtu «LETI» publ., 216 р. (in Russian).
  10. Revay, M. and Liska, M. (2017). OODA loop in command & control systems. In: Communication and Information Technologies (KIT): Vysoke Tatry, Slovakia, IEEE, 1–4 p.
  11. IEEE 1516-2010. (2018) IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA). Framework and Rules. [online]. Available at: https://standards.ieee.org/standard/1516-2010.html [Accessed 06 May 2019].
  12. Postnikov, E. and Matrosov, V. (2018). Modelirovanie slozhnykh naturnykh eksperimentov na osnove HLA-tekhnologii [Modeling of complex field experiments based on HLA-technology]. In: Sbornik dokladov XXI Mezhdunarodnoj konferencii po mjagkim vychislenijam i izmerenijam SCM'2018 [Proceedings of International Conference on Soft Computations and Measurements], pp.542-545. (in Russian).
  13. Ilachinski, A. (2004). Artificial War: Multiagent-based Simulation Of Combat. World Scientific Pub Co Inc. 784 p.
  14. Wang, X. and Zhang, L. (2011). Multi-Agent Systems Simulation Base on HLA Framework. Advances in Automation and Robotics, 2, pp.339-346.
  15. Hodicky, J. (ed.) (2014). Modelling and Simulation for Autonomous Systems. International Workshop on Modelling and Simulation for Autonomous Systems (MESAS). Italy, Rome: Springer. 388 p.
  16. Cil, I. and Mala, M. (2009). MABSIM: A multi agent based simulation model of military unit combat. In: Second International Conference on the Applications of Digital Information and Web Technologies. IEEE. pp.731-736.
  17. Artificial Intelligence Applications Institute. (n.d.) ModSAF (Modular Semi-Automated Forces). [online]. Available at: http://www.aiai.ed.ac.uk/~arpi/SUO/MODULES/modsaf.html [Ac-cessed 06 May 2019].
  18. Manojlovich, J. et al. (2003). UTSAF: a multi-agent-based framework for supporting military-based distributed interactive simulations in 3D virtual environments. In: Proceedings of the 2003 Winter Simulation Conference. IEEE. pp.960-968.
  19. Luke, S. et al. (2005). MASON: A Multiagent Simulation Environment. SIMULATION: Transactions of The Society for Modeling and Simulation International (SIMUL-T SOC MOD SIM). DBLP, 81(7), pp.517-527.
  20. Panteleev, M., Kokhtenko, N. and Lebedev, S. (2012). Sreda imitatsionnogo modelirovaniya agentnykh sistem real'nogo vremeni [Real-time agent systems simulation environment]. Nauchno-tehnicheskij vestnik informacionnyh tehnologij, mehaniki i optiki, 1(77), pp.53–58. (in Russian).
  21. Panteleev, M., Kokhtenko, N. and Lebedev, S. (2014). Sreda imitatsionnogo modeliro-vaniya gruppovykh deistvii avtonomnykh BPLA [Simulation environment for group actions of Autonomous UAVs]. In: Trudy 14 nacional'noj konferencii po iskusstvennomu intellektu s mezhdunarodnym uchastiem KII-2014. Kazan: RIC «Shkola» publ., pp.324-332. (in Russian).
  22.  Lebedev, S. and Panteleev, M. (2016). The ontological subsystem design assessment of the situation of intelligent agents [Ontologicheskoe proektirovanie podsistemy otsenki obstanovki intellektual'nykh agentov]. Ontologija proektirovanija. 6(3), pp.297-316. (in Russian).
  23. Panteleev, M. (2014). Formal'naya model' operezhayushchego iterativnogo planirovaniya deistvii intellektual'nykh agentov real'nogo vremeni [Formal model of advanced iterative planning of actions of intelligent real-time agents]. In: Trudy 14 nacional'noj konferencii po iskusstvennomu intellektu s mezhdunarodnym uchastiem KII-2014. Kazan: RIC «Shkola» publ., pp.323-333. (in Russian).
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