Using Unmanned Aerial Systems in Military Operations for Autonomous Reconnaissance

  • Petr StodolaEmail author
  • Jaroslav Kozůbek
  • Jan Drozd
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11472)


The article deals with modern technologies to support military operations in order to yield new innovations and development in the area of security and sustainability of military units. The article is divided into two key parts. The first part presents the model of the Autonomous Aerial Reconnaissance Problem (AARP). Firstly, the basic features and principles of the model are discussed. The AARP can be seen as a well-known Multi-Depot Vehicle Routing Problem (MDVRP); however, the different optimal criterion is used. Thus, the AARP is formulated for the first time as a new problem. Then, the basic aspects of the original metaheuristic solution proposed by the authors to the AARP is introduced. Finally, the algorithm is verified on the benchmark instances to show its effectiveness. The second key part of the article deals with the tactical aspects of the AARP; the use of the model is shown on the platoon level within a chosen tactical activity.


Unmanned aerial systems Reconnaissance operations Meta-heuristic algorithms Ant colony optimization Multi-depot routing problem Raid 


  1. 1.
    Hodicky, J., Prochazka, D., Prochazka, J.: Training with and of autonomous system – modelling and simulation approach. In: Mazal, J. (ed.) MESAS 2017. LNCS, vol. 10756, pp. 383–391. Springer, Cham (2018). Scholar
  2. 2.
    Stodola, P., Mazal, J.: Tactical decision support system to aid commanders in their decision-making. In: Hodicky, J. (ed.) MESAS 2016. LNCS, vol. 9991, pp. 396–406. Springer, Cham (2016). Scholar
  3. 3.
    Blaha, M., et al.: Perspective method for determination of fire for effect in tactical and technical control of artillery units. In: International Conference on Informatics in Control, Automation and Robotics, Lisboa, pp. 249–254 (2016)Google Scholar
  4. 4.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ho, W., et al.: A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intell. 21, 548–557 (2008)CrossRefGoogle Scholar
  6. 6.
    Sharma, N., Monika, M.: A literature survey on multi-depot vehicle routing problem. Int. J. Sci. Res. Dev. 3(4), 1752–1757 (2015)Google Scholar
  7. 7.
    Gulczynski, D., Golden, B.L., Wasil, E.: The multi-depot split delivery vehicle routing problem: an integer programming-based heuristic, new test problems, and computational results. Comput. Ind. Eng. 61(3), 794–804 (2011)CrossRefGoogle Scholar
  8. 8.
    Imran, A.: A variable neighborhood search-based heuristic for the multi-depot vehicle routing problem. Jurnal Teknik Industri 15(2), 95–102 (2011)Google Scholar
  9. 9.
    Reiners, Ch.: Constraint programming-based heuristics for the multi-depot vehicle routing problem with a rolling planning horizon. Ph.D. thesis, University of Duisburg-Essen, Duisburg (2015)Google Scholar
  10. 10.
    Bae, H., Moon, I.: Multi-depot vehicle routing problem with time windows considering delivery and installation vehicles. Appl. Math. Model. 40(13–14), 6536–6549 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Maischberger, M., Cordeau, J.-F.: Solving variants of the vehicle routing problem with a simple parallel iterated tabu search. In: Pahl, J., Reiners, T., Voß, S. (eds.) INOC 2011. LNCS, vol. 6701, pp. 395–400. Springer, Heidelberg (2011). Scholar
  12. 12.
    Vidal, T., et al.: A hybrid genetic algorithm for multi-depot and periodic vehicle routing problems. Oper. Res. 60(3), 611–624 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yalian, T.: An improved ant colony optimization for multi-depot vehicle routing problem. Int. J. Eng. Technol. 8(5), 385–388 (2016)CrossRefGoogle Scholar
  14. 14.
    Gao, J.: Automobile chain maintenance parts delivery problem using an improved ant colony algorithm. Adv. Mech. Eng. 8(9), 1–13 (2016)CrossRefGoogle Scholar
  15. 15.
    Ma, Y., Han, J., Kang, K., Yan, F.: An improved ACO for the multi-depot vehicle routing problem with time windows. In: Xu, J., Hajiyev, A., Nickel, S., Gen, M. (eds.) Proceedings of the Tenth International Conference on Management Science and Engineering Management. AISC, vol. 502, pp. 1181–1189. Springer, Singapore (2017). Scholar
  16. 16.
    Geiger, B.: Unmanned aerial vehicle trajectory planning with direct methods. Ph.D. thesis, Pennsylvania State University, Old Maine (2009)Google Scholar
  17. 17.
    Wang, H., et al.: Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system. Chin. J. Aeronaut. 28(1), 229–239 (2015)CrossRefGoogle Scholar
  18. 18.
    Zhan, W., et al.: Efficient UAV path planning with multiconstraints in a 3D large battlefield environment. In: Mathematical Problems in Engineering, pp. 1–12 (2014)Google Scholar
  19. 19.
    Fu, S.-Y., et al.: Path planning for unmanned aerial vehicle based on genetic algorithm. In: International Conference on Cognitive Informatics & Cognitive Computing, pp. 140–144. IEEE (2012)Google Scholar
  20. 20.
    Keller, J.: Coordinated path planning for fixed-wing UAS conducting persistent surveillance missions. IEEE Trans. Autom. Sci. Eng. 14(1), 17–24 (2017)CrossRefGoogle Scholar
  21. 21.
    Lu, L., Zong, C., Lei, X., Chen, B., Zhao, P.: Fixed-wing UAV path planning in a dynamic environment via dynamic RRT algorithm. In: Zhang, X., Wang, N., Huang, Y. (eds.) ASIAN MMS 2016, CCMMS 2016. LNEE, vol. 408, pp. 271–282. Springer, Singapore (2016). Scholar
  22. 22.
    Da Silva Arantes, J., et al.: Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int. J. Artif. Intell. Tools 26(1), 1–30 (2017)CrossRefGoogle Scholar
  23. 23.
    Ingersol, B.T., et al.: UAV path-planning using bezier curves and a receding horizon approach. In: AIAA Modeling and Simulation Technologies Conference, Washington, D.C., 2016, pp. 1–14 (2016)Google Scholar
  24. 24.
    Cekmez, U., Ozsiginan, M., Sahingoz, O.K.: Multi-UAV path planning with parallel genetic algorithms on CUDA architecture. In: Genetic and Evolutionary Computation Conference Companion, Denver, pp. 1079–1086 (2016)Google Scholar
  25. 25.
    Stodola, P., Mazal, J.: Applying the ant colony optimization algorithm to the capacitated multi-depot vehicle routing problem. Int. J. Bio-Inspired Comput. 8(4), 228–233 (2016)CrossRefGoogle Scholar
  26. 26.
    NEO Web. Networking and Emerging Optimization. University of Malaga, Spain (2018). Accessed 7 July 2018
  27. 27.
    Drozd, J., Flasar, Z.: Unmanned aerial vehicle influence of troops leading procedure. In: Applied Technical Sciences and Advanced Military Technologies, Sibiu, Romania “Nikolae Balcescu” Land Forces Academy, pp. 155–162 (2017)Google Scholar
  28. 28.
    Kozubek, J., Flasar, Z.: Possibilities of verification the required capabilities according to NATO network enabled capabilities concept. Croatian J. Educ. 14(1), 87–98 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of DefenceBrnoCzech Republic

Personalised recommendations