Skip to main content

Research on Multi-UAV Swarm Control Based on Olfati-Saber Algorithm with Variable Speed Virtual Leader

  • Conference paper
  • First Online:
IoT as a Service (IoTaaS 2020)

Abstract

The high efficiency of control between multiple drones has become a hot topic of research nowadays. Due to the increased demand for combat operations and the increasing number of drones, the efficiency of control between multiple drones has become a hot research topic. Drawing on the principles of some communication in swarm intelligence, it is of great significance for realizing the autonomous cooperative control between UAVs. Learning from the Olfati-Saber algorithm, this paper proposes an optimized algorithm with virtual leaders, in order to make the group speed converge faster and more stable. Then, this paper also shows the impact of variable-speed virtual leaders on complex drone communication systems. Subsequently, two models are simply analyzed and compared with each other in the article. Through the simulation, we prove the effectiveness of certain variable speed virtual leaders for decentralized clusters of complex UAV systems, which improves the application of Olfati-Saber model in practice.

This work was supported in part by the National Key Research and Development Program (Grant Nos. 2016YFB1200100), and the National Natural Science Foundation of China (NSFC) (Grant Nos. 61827901 and 91738301).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

References

  1. Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A., Granado-Criado, J.M.: A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design. Eng. Appl. Artif. Intell. 26(9), 2045–2057 (2013)

    Article  Google Scholar 

  2. Ok, C., Lee, S., Kumara, S.: Group preference modeling for intelligent shared environments: social welfare beyond the sum. Inf. Sci. 278, 588–598 (2014)

    Article  MathSciNet  Google Scholar 

  3. Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51(3), 401–420 (2006)

    Article  MathSciNet  Google Scholar 

  4. Su, H., Wang, X.F., Yang, W.: Flocking in multi-agent systems with multiple virtual leaders. Asian J. Control 10(2), 238–245 (2008)

    Article  MathSciNet  Google Scholar 

  5. Luo, X.Y., Li, S.B., Guan, X.P.: Flocking algorithm with multi-target tracking for multi-agent systems. Pattern Recogn. Lett. 31(9), 800–805 (2010)

    Article  Google Scholar 

  6. Shi, G.D., Hong, Y.G., Johansson, K.H.: Connectivity and set tracking of multi-agent systems guided bu multiple moving leaders. IEEE Trans. Autom. Control 57(3), 663–676 (2012)

    Article  Google Scholar 

  7. Liu, J., Ren, X.M., Ma, H.B.: Adaptive swarm optimization for locating and tracking multiple targets. Appl. Soft Comput. 12(11), 3656–3670 (2012)

    Article  Google Scholar 

  8. Hutchison, M.G.: A method for estimating range requirements of tactical reconnaissance UAVs. In: AIAA’s 1st Technical Conference and Workshop on Unmanned Aerospace Vehicles, Portsmouth, Virginia, pp. 120–124 (2002)

    Google Scholar 

  9. Szczerba, R.J., Galkowski, P., Glicktein, I.S., et al.: Robust algorithm for real-time route planning. IEEE Trans. Aerosp. Electron. Syst. 36(3), 869–878 (2000)

    Article  Google Scholar 

  10. Jevtić A, Andina D, Jaimes A., et al.: Unmanned aerial vehicle route optimization using ant system algorithm. In: 2010 5th International Conference on System of Systems Engineering (So SE), pp. 1–6. IEEE (2010)

    Google Scholar 

  11. Nygard, K.E., Chandler, P.R., Pachter, M.: Dynamic network flow optimization models for air vehicle resource allocation. In: Proceedings of the 2001 American Control Conference, vol. 3, pp. 1853–1858. IEEE (2001)

    Google Scholar 

  12. Wei, L., Wei, Z.: Method of tasks allocation of multi-UAVs based on particles swarm optimization. Control Decis. 25(9), 1359–1363 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanqi Jing .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jing, Y. (2021). Research on Multi-UAV Swarm Control Based on Olfati-Saber Algorithm with Variable Speed Virtual Leader. In: Li, B., Li, C., Yang, M., Yan, Z., Zheng, J. (eds) IoT as a Service. IoTaaS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-67514-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67514-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67513-4

  • Online ISBN: 978-3-030-67514-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics