Advertisement

Modelling and Optimization of the Air Operational Manoeuvre

  • Agostino G. BruzzoneEmail author
  • Josef Procházka
  • Libor Kutěj
  • Dalibor Procházka
  • Jaroslav Kozůbek
  • Radomir Scurek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11472)

Abstract

Increasing complexity of the operational environment and advanced technology implementation in combat will probably lead to a serious limitation of human performance in all operational domains and activities in the future. With except of the clear indications, that tactical robotics will outperform human soldiers in many routine tasks on the battlefield, the area of operational decision making (resistible for decades to some automation) seems to be slowly approaching to the same stage. Presented article discusses the fundamental theory of optimization of the air operational maneuver and present the approach to the solution. The solution is highly theoretical and uses a modelling and simulation as an experimental platform to the visualization and evaluation of solution. The problem of air operational maneuver is specific in this case by many variables imposed on initial parametrization of the task (starting and destination point could not be known at the beginning, only “air operational” area should be selected) and very wide search of possible courses of action and the best “multi criteria” choice identification.

Keywords

UAV Safety maneuver modelling ISR Optimization Air maneuverer 

References

  1. 1.
    Geiger, B.: Unmanned aerial vehicle trajectory planning with direct methods. A Dissertation in Aerospace Engineering. The Pennsylvania State University, Pennsylvania, USA (2009)Google Scholar
  2. 2.
    Waseem, A.K.: Safe trajectory planning techniques for autonomous air vehicles. A dissertation work. University of Leicester, United Kingdom (2005)Google Scholar
  3. 3.
    Tsourdos, A., White, B., Shanmugavel, M.: Cooperative Path Planning of Unmanned Aerial Vehicles, p. 214. Wiley, Hoboken (2010). ISBN: 978-0-470-74129-0Google Scholar
  4. 4.
    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
  5. 5.
    Qu, Y.H., Pan, Q., Yan, Y.G.: Flight path planning of UAV based on heuristically search and genetic algorithms. In: Proceedings of the IEEE 32nd Annual Conference, pp. 45–50 (2005)Google Scholar
  6. 6.
    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
  7. 7.
    Kress, M.: Operational Logistics: The Art and Science of Sustaining Military Operations. Springer, Heidelberg (2002).  https://doi.org/10.1007/978-1-4615-1085-7CrossRefGoogle Scholar
  8. 8.
    Rybar, M.: Modelovanie a simulacia vo vojenstve. Ministerstvo obrany Slovenskej republiky, Bratislava (2000)Google Scholar
  9. 9.
    Washburn, A., Kress, M.: Combat Modeling: International Series in Operations Research & Management Science. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Mokrá, I.: Modelový přístup k rozhodovacím aktivitám velitelů jednotek v bojvých operacích. Disertační práce. Brno: Univerzita obrany v Brně, Fakulta ekonomiky a managementu. 120 s (2012)Google Scholar
  11. 11.
    Mazal, J., Stodola, P., Procházka, D., Kutěj, L., Ščurek, R., Procházka, J.: Modelling of the UAV safety manoeuvre for the air insertion operations. In: Hodicky, J. (ed.) MESAS 2016. LNCS, vol. 9991, pp. 337–346. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47605-6_27CrossRefGoogle Scholar
  12. 12.
    Rybansky, M.: Modelling of the optimal vehicle route in terrain in emergency situations using GIS data. In: 8th International Symposium of the Digital Earth (ISDE8) 2013, Kuching, Sarawak, Malaysia 2014 IOP Conference Series: Earth Environmental Science, vol. 18, p. 012071 (2014).  https://doi.org/10.1088/1755-1315/18/1/012131. ISSN 1755-1307
  13. 13.
    Rybanský, M., Vala, M.: Relief impact on transport. In.: ICMT 2009 - International Conference on Military Technologies 2009, Brno, Czech Republic, 9 p. (2009). ISBN 978-80-7231-649-6 (978-80-7231-648-9 CD)Google Scholar
  14. 14.
    Drozd, J., Stodola, P., Křišťálová, D., Kozůbek, J.: Experiments with the uas reconnaissance model in the real environment. In: Mazal, J. (ed.) MESAS 2017. LNCS, vol. 10756, pp. 340–349. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76072-8_24CrossRefGoogle Scholar
  15. 15.
    Bruzzone, A.G.: MS2G as Pillar for Developing Strategic Engineering as a New Discipline for Complex Problem Solving. Keynote Speech at I3 M, Budapest, September 2018Google Scholar
  16. 16.
    Bruzzone, A.G., Massei, M.: Simulation-based military training. In: Mittal, S., Durak, U., Ören, T. (eds.) Guide to Simulation-Based Disciplines. SFMA, pp. 315–361. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61264-5_14CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Agostino G. Bruzzone
    • 1
    Email author
  • Josef Procházka
    • 2
  • Libor Kutěj
    • 2
  • Dalibor Procházka
    • 2
  • Jaroslav Kozůbek
    • 2
  • Radomir Scurek
    • 3
  1. 1.University of GenoaGenoaItaly
  2. 2.University of DefenceBrnoCzech Republic
  3. 3.WSB Uniwersity Dąbrowa GórniczaDąbrowa GórniczaPoland

Personalised recommendations