Modelling of the UAV Safety Manoeuvre for the Air Insertion Operations

  • Jan MazalEmail author
  • Petr Stodola
  • Dalibor Procházka
  • Libor Kutěj
  • Radomír Ščurek
  • Josef Procházka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9991)


Tempo and complexity of the contemporary asymmetric battlefield is on the increase and time for a certain component delivery (ammunition, medical kit, vaccine and so on), for instance in the special operations, could be critical. Usually, the only way in these situations is a fast air delivery of concrete material to the “hot” destination zone. Contemporary air insertion in that case is usually performed by manned or unmanned (if available) system with human intuitive manoeuvre planning supported by information from ISR systems. In this case, there is almost impossible to achieve a fast, detailed and mathematically optimal solution with the real time implementation to the UAV control system (autopilot). The article describes a modelling approach which leads to high automation and optimal (autonomous) reasoning in case of 3D UAV path planning, respecting the operational situation in the area, manoeuvre limits of the UAV and potential threat in the operational area. The solution is based on detailed operational area 3D modelling, known and unknown probabilistic threat simulation and its capability estimation, quantification of safety area parameters and large 3D (multi-criteria) safety matrix development, criterial function and boundary condition specification, UAV air manoeuvre and constraints algorithm development, optimal UAV path search and operational evaluation.


UAV Safety manoeuvre modelling ISR Optimization Air insertion 


  1. 1.
    Hodicky, J., Frantis, P.: Decision support system for a commander at the operational level. In: Dietz J.L.G. (ed.) KEOD 2009 - Proceedings of International Conference on Knowledge Engineering and Ontology Development, Funchal - Madeira, October 2009, pp. 359–362. INSTICC Press (2009). ISBN 978-989-674-012-2Google Scholar
  2. 2.
    Hodicky, J., Frantis, P.: Using simulation for prediction of units movements in case of communication failure. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energ. Electr. Commun. Eng. 5(7), 796–798 (2011)Google Scholar
  3. 3.
    Hodicky, J.: Modelling and simulation in the autonomous systems’ domain- current status and way ahead. In: Hodicky, J. (ed.) MESAS 2015. Lecture Notes in Computer Science, vol. 9055, pp. 17–23. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  4. 4.
    Geiger, B.: Unmanned aerial vehicle trajectory planning with direct methods. A dissertation in Aerospace Engineering, The Pennsylvania State University, Pennsylvania, USA (2009)Google Scholar
  5. 5.
    Kamal, W.A.: Safe trajectory planning techniques for autonomous air vehicles. A dissertation work, University of Leicester, United Kingdom (2005)Google Scholar
  6. 6.
    Tsourdos, A., White, B., Shanmugavel, M.: Cooperative Path Planning of Unmanned Aerial Vehicles, pp. 1–214. Wiley, Hoboken (2010). ISBN 978-0-470-74129-0CrossRefGoogle Scholar
  7. 7.
    Duan, H.B., Ma, G.J., Wang, D.B., Yu, X.F.: An improved ant colony algorithm for solving continuous space optimization problems. J. Syst. Simul. 19(5), 974–977 (2007)Google Scholar
  8. 8.
    Yao, H.Q., Quan P., Jian, G.Y.: Flight path planning of UAV based on heuristically search and genetic algorithms. In: Proceedings of IEEE 32nd Annual Conference, pp. 45–50 (2005)Google Scholar
  9. 9.
    Liu, C.A., Li, W.J., Wang, H.P.: Path planning for UAVs based on ant colony. J. Air Force Eng. Univ. 2(5), 9–12 (2004)Google Scholar
  10. 10.
    Kress, M.: Operational Logistics: The Art and Science of Sustaining Military Operations. Springer, Berlin (2002)CrossRefGoogle Scholar
  11. 11.
    Rybar, M.: Modelovanie a simulacia vo vojenstve. Ministerstvo obrany Slovenskej republiky, Bratislava (2000)Google Scholar
  12. 12.
    Washburn, A., Kress, M.: Combat Modeling. International Series in Operations Research & Management Science. Springer, Berlin (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Mokrá, I.: Modelový přístup k rozhodovacím aktivitám velitelů jednotek v bojvých operacích. Disertační práce. Univerzita obrany v Brně, Fakulta ekonomiky a managementu, Brno (2012). 120 sGoogle Scholar
  14. 14.
    Binar, T., Sukáč, J., Šilinger, K., Zatloukal, M., Rolc, S.: The steel ballistic resistance directly affecting logistics-related expenditures. In: 16th International Conference on Advanced Batteries, Accumulators and Fuel Cells, ABAF 2015, pp. 187–196. Electrochemical Society Inc., USA (2015). ISSN 1938-5862. ISBN 978-1-60768-539-5Google Scholar
  15. 15.
    Binar, T., Dvořák, I., Kadlec, J., Sukáč, J., Rolc, S., Křesťan, J.: Material characteristics of plastic deformation in high-strength steel. Adv. Mil. Technol. 9(2), 33–39 (2014). ISSN 1802-2308Google Scholar
  16. 16.
    Michálek, J., Sedlačík, M., Doudová, L.: A comparison of two parametric ROC curves estimators in binormal model. In: Proceedings of 23rd International Conference Mathematical methods in Economics 2005. : GAUDEAMUS Univerzita Hradec Králové, Hradec Králové, pp. 256–261 (2005). 11 s, ISBN 80-7041-53Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jan Mazal
    • 1
    Email author
  • Petr Stodola
    • 1
  • Dalibor Procházka
    • 1
  • Libor Kutěj
    • 2
  • Radomír Ščurek
    • 3
  • Josef Procházka
    • 1
  1. 1.University of DefenceBrnoCzech Republic
  2. 2.Ministry of Defence of the Czech RepublicPragueCzech Republic
  3. 3.Faculty of Security EngineeringVŠB-Technical University OstravaOstravaCzech Republic

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