Skip to main content
SpringerLink
Log in

Application of Genetic Algorithm for Vector Field Guidance Optimization in a UAV Collective Circumnavigation Scenario

  • Conference paper
  • First Online:
Robotics in Natural Settings (CLAWAR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 530))

Included in the following conference series:

  • 1458 Accesses

Abstract

Optimizing the trajectories of autonomous unmanned aerial vehicles (UAVs) in a decentralized cooperative tracking of a ground target is not a trivial undertaking. In this case, the UAV formation is a complex interconnected nonlinear system. This paper investigates a genetic algorithm for optimizing the trajectories of UAVs engaged in cooperative target tracking by means of vector field guidance, thus performing collective circumnavigation. Computational modeling shows that the genetic algorithm can effectively address trajectory optimization. Post-optimization reduction in the fitness function value is noted. Another finding is that it is necessary, when tuning the UAV heading controllers, to minimize not only the error of distance to the circular path around the target but also the relative inter-UAV distance error.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Harinarayana, T., Hota, S., Kushwaha, R.: Vector field guidance for standoff target tracking. Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng. (2022). https://doi.org/10.1177/09544100211072320

  2. Vishnevsky, V., Larionov, A., Zvyagin, M., Dinh, T.D., Ovchinnikov, A., Kirichek, R.: Software development for controlling a group of UAVs. ACM Int. Conf. Proc. Ser. (2020). https://doi.org/10.1145/3440749.3442614

    Article  Google Scholar 

  3. Izhboldina, V., Lebedev, I., Shabanova, A.: Approach to UAV swarm control and collision-free reconfiguration. In: Ronzhin, A., Shishlakov, V. (eds.) Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings.” SIST, vol. 187, pp. 81–92. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5580-0_6

    Chapter  Google Scholar 

  4. Bassolillo, S.R., Blasi, L., D’Amato, E., Mattei, M., Notaro, I.: Decentralized triangular guidance algorithms for formations of UAVs. Drones 2022 6, 7 (2021). https://doi.org/10.3390/DRONES6010007

  5. Ollervides-Vazquez, E.J., Rojo-Rodriguez, E.G., Garcia-Salazar, O., Amezquita-Brooks, L., Castillo, P., Santibañez, V.: A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles. Int. J. Micro Air Veh. 12, 175682932097357 (2020). https://doi.org/10.1177/1756829320973579

    Article  Google Scholar 

  6. Muslimov, T.Z., Munasypov, R.A.: Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC. Aircr. Eng. Aerosp. Technol. 93, 1204–1212 (2021). https://doi.org/10.1108/AEAT-02-2021-0058

    Article  Google Scholar 

  7. Diveev, A., Shmalko, E.: Hybrid evolutionary algorithm for synthesized optimal control problem for group of interacting robots. In: 2019 6th Int. Conf. Control. Decis. Inf. Technol. CoDIT 2019, pp. 876–881 (2019). https://doi.org/10.1109/CODIT.2019.8820344

  8. Konstantinov, S., Diveev, A.: Evolutionary algorithms for optimal control problem of mobile robots group interaction. In: Olenev, N.N., Evtushenko, Y.G., Jaćimović, M., Khachay, M., Malkova, V. (eds.) OPTIMA 2021. CCIS, vol. 1514, pp. 123–136. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92711-0_9

    Chapter  Google Scholar 

  9. Wilburn, B.K., Perhinschi, M.G., Wilburn, J.N.: A modified genetic algorithm for UAV trajectory tracking control laws optimization. Int. J. Intell. Unmanned Syst. 2, 58–90 (2014). https://doi.org/10.1108/IJIUS-03-2014-0002

    Article  Google Scholar 

  10. Choutri, K., Lagha, M., Dala, L.: Multi-layered optimal navigation system for quadrotor UAV. Aircr. Eng. Aerosp. Technol. 92, 145–155 (2019). https://doi.org/10.1108/AEAT-12-2018-0313

    Article  Google Scholar 

  11. Darrah, M., et al.: A flexible genetic algorithm system for multi-UAV surveillance: algorithm and flight testing. Unmanned Syst. 3, 49–62 (2015). https://doi.org/10.1142/S2301385015500041

    Article  Google Scholar 

  12. Wu, X., Yin, Y., Xu, L., Wu, X., Meng, F., Zhen, R.: Multi-UAV task allocation based on improved genetic algorithm. IEEE Access. 9, 100369–100379 (2021). https://doi.org/10.1109/ACCESS.2021.3097094

    Article  Google Scholar 

  13. Gutierrez-Martinez, M.A., Rojo-Rodriguez, E.G., Cabriales-Ramirez, L.E., Reyes-Osorio, L.A., Castillo, P., Garcia-Salazar, O.: Collision-free path planning based on a genetic algorithm for quadrotor UAVs. In: 2020 Int. Conf. Unmanned Aircr. Syst. ICUAS 2020, pp. 948–957 (2020). https://doi.org/10.1109/ICUAS48674.2020.9213956

  14. Pehlivanoglu, Y.V., Pehlivanoglu, P.: An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems. Appl. Soft Comput. 112, 107796 (2021). https://doi.org/10.1016/j.asoc.2021.107796

    Article  Google Scholar 

  15. Najm, A.A., Ibraheem, I.K., Azar, A.T., Humaidi, A.J.: Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System. Sensors 2020, vol. 20, pp. 3576 (2020). https://doi.org/10.3390/S20123576

  16. Mondal, S., Tsourdos, A.: Autonomous addition of agents to an existing group using genetic algorithm. Sensors 20, 6953 (2020). https://doi.org/10.3390/S20236953

  17. Bożko, A., Ambroziak, L., Pawluszewicz, E.: Genetic algorithm for parameters tuning of two stage switching controller for UAV autonomous formation flight. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) AUTOMATION 2021. AISC, vol. 1390, pp. 154–165. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-74893-7_16

    Chapter  Google Scholar 

  18. Kim, M.: Error dynamics-based guidance law for target observation using multiple UAVs with phase angle constraints via evolutionary algorithms. J. Control Autom. Electr. Syst. 32(6), 1510–1520 (2021). https://doi.org/10.1007/s40313-021-00790-1

    Article  Google Scholar 

  19. Beard, R.W., McLain, T.W.: Small Unmanned Aircraft: Theory and Practice. Princeton University Press (2012)

    Google Scholar 

  20. Muslimov, T.Z., Munasypov, R.A.: Coordinated UAV standoff tracking of moving target based on Lyapunov vector fields. In: 2020 International Conference Nonlinearity, Information and Robotics (NIR), pp. 1–5, IEEE (2020). https://doi.org/10.1109/NIR50484.2020.9290189

Download references

Acknowledgements

This work was supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2021-1016).

Author information

Authors and Affiliations

  1. Ufa State Aviation Technical University, K. Marx Str., 12, 450008, Ufa, Russian Federation

    Tagir Muslimov

Authors
  1. Tagir Muslimov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tagir Muslimov .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, University of the Azores, Ponta Delgada, Portugal

    José M. Cascalho

  2. School of Engineering, London South Bank University, London, UK

    Mohammad Osman Tokhi

  3. School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Manuel F. Silva

  4. Faculty of Sciences and Technology, University of the Azores, Ponta Delgada, Portugal

    Armando Mendes

  5. Mechanical, Materials and Manufacturing Engineering (M3), Faculty of Engineering, University of Nottingham, Nottingham, UK

    Khaled Goher

  6. Faculty of Sciences and Technology, University of the Azores, Ponta Delgada, Portugal

    Matthias Funk

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Muslimov, T. (2023). Application of Genetic Algorithm for Vector Field Guidance Optimization in a UAV Collective Circumnavigation Scenario. In: Cascalho, J.M., Tokhi, M.O., Silva, M.F., Mendes, A., Goher, K., Funk, M. (eds) Robotics in Natural Settings. CLAWAR 2022. Lecture Notes in Networks and Systems, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-031-15226-9_31

Download citation

Publish with us

Policies and ethics

Search

Navigation