Skip to main content
Log in

Optimization of UAV Team Routes in the Presence of Alternative and Dynamic Depots

  • SYSTEMS ANALYSIS
  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

The paper considers route optimization problems for unmanned aerial vehicles (UAV), which act as a team when inspecting or supporting a given set of objects in the presence of alternative and dynamic depots (starting and/or landing sites) and resource constraints. Problem definition and mathematical models are proposed. Such problems, in particular, include UAV flight planning problems, which use mobile platforms as a depot. The optimization criteria are both the total length of the routes and the number of UAVs involved. Algorithms for solving formulated combinatorial optimization problems based on ant colony optimization, tabu search, and exhaustive search have been developed and implemented. The results of the computational experiment are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S. S. Ponda, L. B. Johnson, A. Geramifard, and J. P. How, “Cooperative mission planning for multi-UAV teams,” in: K. Valavanis, G. Vachtsevanos (eds.), Handbook of Unmanned Aerial Vehicles, Springer, Dordrecht (2015), pp. 1447–1490.

    Chapter  Google Scholar 

  2. A. Otto, N. Agatz, J. Campbell, B. Golden, and E. Pesch, “Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey,” Networks, Vol. 72, Iss. 4, 411–458 (2018).

    Article  MathSciNet  Google Scholar 

  3. V. P. Horbulin, “Ensuring the defense and security of Ukraine: Current problems and ways of their solution,” Visn. Nac. Akad. Nauk Ukr., No. 9, 3–18 (2019).

  4. W. P. Coutinho, M. Battarra, and J. Fliege, “The unmanned aerial vehicle routing and trajectory optimization problem, a taxonomic review,” Computers & Industrial Engineering, Vol. 120, 116–128 (2018).

    Article  Google Scholar 

  5. Y. Zhao, Z. Zheng, and Y. Liu, “Survey on computational-intelligence-based UAV path planning,” Knowledge-Based Systems, Vol. 158, 54–64 (2018).

    Article  Google Scholar 

  6. L. F. Hulianytskyi and O. V. Rybalchenko, “Formalization and solution of one type of UAV routing problems,” Teoriya Optym. Rishen’, No. 17, 107–114 (2018).

  7. S. Perez-Carabaza, E. Besada-Portas, J. A. Lopez-Orozco, and M. Jesus, “Ant colony optimization for multi-UAV minimum time search in uncertain domains,” Applied Soft Computing, Vol. 62, 789–806 (2018).

    Article  Google Scholar 

  8. U. Cekmez, M. Ozsiginan, and O.K. Sahingoz, “Multi-UAV path planning with multi colony ant optimization,” in: Intern. Conf. on Intelligent Systems Design and Applications (2017, December), Springer, Cham (2017), pp. 407–417.

  9. W. C. Chiang, Y. Li, J. Shang, and T. L. Urban, “Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization,” Applied Energy, Vol. 242, 1164–1175 (2019).

    Article  Google Scholar 

  10. H. Binol, E. Bulut, K. Akkaya, I. Guvenc, “Time optimal multi-UAV path planning for gathering ITS data from roadside units,” in: 88th Vehicular Technology Conference (VTC-Fall) (2018, August), IEEE (2018), pp. 1–5.

  11. C. Xu, H. Duan, and F. Liu, “Chaotic artificial bee colony approach to uninhabited combat air vehicle (UCAV) path planning,” Aerospace Science and Technology, Vol. 14, Iss. 8, 535–541 (2010).

    Article  Google Scholar 

  12. G. Tian, L. Zhang, X. Bai, and B. Wang, “Real-time dynamic track planning of multi-UAV formation based on improved artificial bee colony algorithm,” in: 37th Chinese Control Conference (CCC) (2018, July), IEEE (2018), pp. 10055–10060.

  13. H. Shakhatreh, A. Khreishah, A. Alsarhan, I. Khalil, A. Sawalmeh, and N. S. Othman, “Efficient 3D placement of a UAV using particle swarm optimization,” in: 8th Intern. Conf. on Information and Communication Systems (ICICS) (2017, April), IEEE (2017), pp. 258–263.

  14. R. Austin, Unmanned Aircraft Systems. UAVs Design, Development and Deployment, John Wiley and Sons, West Sussex (2010).

    Book  Google Scholar 

  15. A. Tsourdos, B. White, and M. Shanmugavel, Cooperative Path Planning of Unmanned Aerial Vehicles, John Wiley and Sons, West Sussex (2011).

    Google Scholar 

  16. T. Shima and S. Rasmussen, UAV Cooperative Decision and Control. Challenges and Practical Approaches. SIAM, Philadelphia (2009).

    Book  Google Scholar 

  17. P. Toth and D. Vigo (eds.), Vehicle Routing: Problems, Methods, and Applications, SIAM, Philadelpia (2014).

    MATH  Google Scholar 

  18. K. Braekers, K. Ramaekers, and I. V. Nieuwenhuyse, “The vehicle routing problem: State of the art classification and review,” Computers & Industrial Engineering, Vol. 99, 300–313 (2016).

    Article  Google Scholar 

  19. S. Karakatič and V. Podgorelec, “A survey of genetic algorithms for solving multi depot vehicle routing problem,” Applied Soft Computing, Vol. 27, 519–532 (2015).

    Article  Google Scholar 

  20. M. Soto, M. Sevaux, A. Rossi, and A. Reinholz, “Multiple neighborhood search, tabu search and ejection chains for the multi-depot open vehicle routing problem,” Computers & Industrial Engineering, Vol. 107, 211–222 (2017).

    Article  Google Scholar 

  21. V. P. Horbulin, L. F. Hulianytskyi, and I. V. Sergienko, “Statements and mathematical models of optimization problems for aircraft routes with dynamic depots,” Upravl. Sistemy i Mashiny, No. 1, 3–10 (2019).

  22. T. Husseini, “Gremlins are coming: DARPA enters Phase III of its UAV programme,” Army Technology, July 3, 2018. URL: https://www.army-technology.com/features/gremlins-darpa-uav-programme/.

  23. L. F. Hulianytskyi, “The problem of optimization of vehicle routes with time intervals,” Kompyuternaya Matematika, No. 1, 122–132 (2007).

  24. J. A. Anderson, Discrete Mathematics with Combinatorics, Prentice Hall (2003).

  25. M. Dorigo and T. Stutzle, “Ant colony optimization: Overview and recent advances,” in: Handbook of Metaheuristics, Springer, Cham (2019), pp. 311–352.

  26. T. Stutzle and H. H. Hoos, “MAX-MIN ant system,” Future Generation Computer Systems, Vol. 16, Iss. 8, 889–914 (2000).

  27. L. F. Hulianytskyi and O. Yu. Mulesa, Applied Methods of Combinatorial Optimization [in Ukrainian], VPTs Kyivs’kyi Universytet, Kyiv (2016).

  28. L. F. Hulianytskyi, “A new ant colony optimization algorithm,” in: Modern Informatics: Problems, Achievements and Perspectives of Development: Intern. Conf., dedicated to the 60th Anniversary of the V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, December 13–15, 2017, Kyiv, V. M. Glushkov Institute of Cybernetics, NAS of Ukraine, Kyiv (2017), pp. 41–43.

  29. A. M. Mora, P. Garccía-Sánchez, J. J. Merelo, and P. A. Castillo, “Pareto-based multi-colony multi-objective ant colony optimization algorithms: An island model proposal,” Soft Computing, Vol. 17, Iss. 7, 1175–1207 (2013).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. P. Horbulin.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 2, March–April, 2020, pp. 31–41.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Horbulin, V.P., Hulianytskyi, L.F. & Sergienko, I.V. Optimization of UAV Team Routes in the Presence of Alternative and Dynamic Depots. Cybern Syst Anal 56, 195–203 (2020). https://doi.org/10.1007/s10559-020-00235-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-020-00235-8

Keywords

Navigation