SIMULATION MODEL OF THE SITUATIONAL AWARENESS FORMATION IN A GROUP OF AUTONOMOUS ROBOTS IN THE POTENTIAL THREATS ENVIRONMENT
Abstract
Issues of situation awareness are key in many subject areas from the defense sector to agri-culture. The purpose of the study was to create a model that allows to simulate the processes for-mation of situation awareness information on the current status of a selected geographic area by a group of heterogeneous autonomous robots. They have technical capabilities and equipment for monitoring of any territory. They using in different environments and acting on the principles of self-organization,. It was assumed that the functioning of the robots can be objectively limited passive factors (fog, rain, snow, night, etc.) and the active influence of opposing forces leading to obstacles in solving tasks, including their the non-fulfillment. In developing the model was raised and addressed questions on the distribution of tasks in a group of robots, search and evaluation of potential threats, formation of the routes subject to the limitations terrain and possible resistance, interaction of robots with each other for obtain and provision of necessary information and team-work to the formation and use of situation awareness information in the study area of space. The situation awareness model is presented as an Internet cloud with a given content structure and in-cludes three main sections describing the current state of robots, the state of various fragments of the functioning area of robots, and information from external systems. All the robots of the group can access online this cloud for receiving and placing information about their own state, the state other robots in the group, observable events in a selected fragment of the environment using robots monitoring systems. The local rules of self-organization form in according to the principles of the Internet of Things by way of organizing information/managing robots systems sensors interaction. Robots can assist to each other also. Algorithms of the model are invariant to different subject areas. The model is easily transforming for various environments — terrestrial and air operations using autonomous robots, the functioning of robots in the water area, underwater, ice, etc.
References
2. Mica R. Endsley. Designing For Situation Awareness: CRC Press, CRC Press Taylor & Fran-cis Group. 2016. – 333 p.
3. Massel L.V., Ivanov R.A., Possibility of application of Situational Awareness in energy research // Proceedings of the Workshop on Computer Science and Informational Technologies (CSIT-2010), Russia, Moscow – St. Petersburg, September 13-19. – 2010. – Vol. 1. – P. 185-187.
4. Рудианов Н.А., Хрущев В.С., Рябов А.В., Носков В.П. Использование полуавтономных бое-вых и обеспечивающих роботов для повышения ситуационной осведомленности // Науч-ные труды 3 ЦНИИ МО РФ, Кн. 24. – М.: Изд-во 3 ЦНИИ МО РФ, 2013. – С. 193-196.
5. Кербер О.Б., Начинкина Г.Н., Солонников Ю.И., Шевченко А.М. Методы улучшения ситуационной осведомленности экипажа воздушного судна на взлетно-посадочных ре-жимах // Авиакосмическое приборостроение. – 2016. – № 5. – С. 33-47.
6. Короленко В.А., Синявский В.К.. Верещагин С.И., Гочиев Н.Х. Парадигма сетецентриче-ского управления и ее влияние на процессы управления войсками. – Режим доступа https://docplayer.ru/39861245-Paradigma-setecentricheskogo-upravleniya-i-ee-vliyanie-na-processy-upravleniya-voyskami.html.
7. Cheng S.,Gang F., Yuan F.,Yong W. Decentralized adaptive awareness coverage control for multi-agent networks // Automatica. – 2011. – Vol. 47. – P. 2749-2756.
8. Pinciroli C., Bonani M., Mondada F., Dorigo M. Adaptation and Awareness in Robot Ensem-bles: Scenarios and Algorithms, in M. Wirsing et al. (eds.): Collective Autonomic Systems, LNCS 8998. – 2015. – P. 471-494.
9. Salmon, P., Stanton, N. A., Walker, G. H., & Green, D. Situation awareness measurement: A review of applicability for C4i environments // Applied Ergonomics. – 2016. – Vol. 37 (2). – P. 225-238. DOI: 10.1016/j.apergo.2005.02.001.
10. Situation Awareness With Systems Of Systems. – Springer-Verlag New York. 2013. – 270 p.
11. Швец Е.А. Разработка моделей картирования и патрулирования коллективом беспилот-ных наземных роботов, использующих техническое зрение и эхолокацию: Автореф. дисс. … канд. техн. наук: [Место защиты: Ин-т проблем передачи информации им. А.А. Харкевича РАН]. – 2016. – 139 с. – Режим доступа: http://iitp.ru/upload/content/ 1327/synopsis.pdf.
12. A Cognitive Approach to Situation Awareness: Theory and Application, Simon Banbury (Edi-tor), Routledge, London. – 2st ed. 2016. – 356 p.
13. Abrosimov V. Role Allocation in a Group of Control Objects // in Recent Developments in Intelligent Nature-Inspired Computing, Chapter 10, Heidelberg: Springer-Verlag, IGI Global, 2017. – P. 206-224.
14. Abrosimov V. The Property of Agent’s Sacrifice: Definition, Measure, Effect and Applications // International Journal of Reasoning Based Intelligent Systems. – 2016. – Vol. 8, No. ½. – P. 76-84.
15. Каляев, И.А., Капустян С.Г., Усачев Л.Ж., Метод решения задач распределения целей в группе БЛА сетецентрической системой управления // Известия ЮФУ. Технические науки. – 2016, – № 12 (185). – С. 55-70.
16. Likhachev M., Ferguson D., Gordon G., Stentz A., Thrun T., Anytime Dynamic A*: An Any-time, Replanning Algorithm // Proceedings of the Fifteenth International Conference on Au-tomated Planning and Scheduling (ICAPS 2005), June 5-10, 2005, Monterey, California, USA.
17. Savla K., Frazzoli E., Bullo F. Traveling Salesperson Problems for the Dubins Vehicle // IEEE Transactions on Automatic Control. – Jul. 2008. – Vol. 53, No. 6. – P. 1378-1390.
18. Абросимов В.К., Мочалкин А.Н. Роботы как объекты управления в ландшафте интернета вещей // Труды II-й Военно-научной конференции «Роботизация Вооруженных Сил Российской Федерации». – М., 2017. – C. 93-98.
19. Шешалевич В.В. LPWAN-низкопотребляющие сети большого радиуса действия // Связь для Интернета вещей. Безопасность информационных технологий. – 2017. – Т. 24, № 3. – C. 7-17.
20. Каляев И.А., Гайдук А.Р., Капустян С.Г. Самоорганизация в мультиагентных системах. // Известия ЮФУ. Технические науки. – 2010. – № 3 (104). – С. 14-20.