Motion Strategy by Intelligent Vehicles-Agents Fleet in Unfriendly Environment

  • Viacheslav Abrosimov
  • Vladislav Ivanov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)


This article considers the territory monitoring problems by air vehicles. Vehicles are considered as intelligent agents. A specific feature consists in the inherent antagonism of the motion environment, which is a common situation in practice. Three major conditions of monitoring are formulated, namely, (1) the necessity of repeated solution of the monitoring tasks with varying routes in each cycle, (2) the necessity of online communication among vehicles under their complete independence in decision-making and (3) the possibility of task failure by some vehicles due to constraints imposed by an unfriendly environment. We introduce a group control strategy for a fleet of vehicles performing monitoring. All vehicles-agents receive a given route from a leading agent or calculate and correct the route in the autonomous mode. The efficiency of the suggested approach is demonstrated by monitoring of an emergency situation, viz., a fire in a forest zone approaching a critical object (a nuclear power plant).


Intelligent agent Air vehicle Group control Strategy Monitoring Unfriendly environment 


  1. 1.
    Abrosimov, V.K.: Group Motion of Intelligent Aircrafts in an Unfriendly Environment. Nauka Publishing House, Moscow (2013). (in Russian)Google Scholar
  2. 2.
    Golden, B.L., Raghavan, S., Wasil, E.A. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Springer (Operations Research/Computer Science Interfaces Series.) (2008)Google Scholar
  3. 3.
    Barbarosogu, G., Ozdamar, L., Cevik, A.: An interactive approach or hierarchical analysis of helicopter logistics in relief operation. Eur. J. Oper. Res. 140, 118–133 (2002)CrossRefGoogle Scholar
  4. 4.
    Barrie, B.M., Ayechew, M.A.: A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 30, 787–800 (2003)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Du, M., Yi, H.: Research on multi-objective emergency logistics vehicle routing problem under constraint conditions J. Ind. Eng. Manage. JIEM 6(1), 258–266 (2013)Google Scholar
  6. 6.
    Hsueh1, C.-F., Chen, H.-K., Chou, H.-W.: Dynamic vehicle routing for relief logistics in natural disasters in vehicle routing problem. In: Caric, H., Gold, H. (eds.), pp 71–84. I-Tech Education and Publishing KG, Vienna, Austria (2008)Google Scholar
  7. 7.
    Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press (2000)Google Scholar
  8. 8.
    Systems, Multiagent: Algorithmic, Game-Theoretic and Logical Foundations. Cambridge University Press, Hardcover (2008)Google Scholar
  9. 9.
    Fenghui, R.: Autonomous agent negotiations strategies in complex environment. PhD, School of Computer Science and Software Engineering, Faculty of Engineering, University of Wollongong (2010)Google Scholar
  10. 10.
  11. 11.
    Holvoet, T., Valckenaers P.: Exploiting the environment for coordinating agent intentions. In: Proceedings of Third International Workshop on Environments for Multi-Agent Systems (E4MAS06). Hakodate, Japan, Springer. (2006)Google Scholar
  12. 12.
    Weyns, D., Holvoet, T.: Architectural design of a situated multi-agent system for controlling automatic guided vehicles. Int. J. Agent-Oriented Softw. Eng. 1(2), 90–128 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Moscow Aviation Institute (National Research University)MoscowRussia

Personalised recommendations