Skip to main content

Evolutionary Algorithms for Optimal Control Problem of Mobile Robots Group Interaction

  • Conference paper
  • First Online:
Advances in Optimization and Applications (OPTIMA 2021)

Abstract

An optimal control problem of mobile robots group interaction on a plane with hourglass-shaped phase constraints is presented. Hourglass-shaped phase constraints can be represented as checkpoints that must be traversed by any or all of controlled objects. The modern evolutionary algorithms are used for searching the control that provides passage of all checkpoints by all robots of the group in minimum time. In a computational experiment the performance of hybrid evolutionary algorithm for solving this task is considered for mobile robots being launched simultaneously.

The research was supported by the Russian Science Foundation (project No 19-11-00258).

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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. Evtushenko, Y.G.: Optimization and Rapid Automatic Differentiation. Computing Center of RAS, Moscow (2013). (in Russian)

    Google Scholar 

  2. Polak, E.: Computational Methods in Optimization. A Unified Approach, New York (1971)

    Google Scholar 

  3. Grachev, I.I., Evtushenko, Y.G.: A library of programs for solving optimal control problems. USSR Comput. Math. Math. Phys. 19(2), 99–119 (1979)

    Article  Google Scholar 

  4. Diveev, A.I., Konstantinov, S.V.: Study of the practical convergence of evolutionary algorithms for the optimal program control of a wheeled robot. J. Comput. Syst. Sci. Int. 57(4), 561–580 (2018)

    Article  Google Scholar 

  5. Konstantinov, S.V., Diveev, A.I., Balandina, G.I., Baryshnikov, A.A.: Comparative research of random search algorithms and evolutionary algorithms for the optimal control problem of the mobile robot. Procedia Comput. Sci. 150, 462–470 (2019)

    Article  Google Scholar 

  6. Karpenko, A.P.: Modern Algorithms of Search Engine Optimization. Nature-Inspired Optimization Algorithms, MGTU n.a. N.E. Bauman, Moscow (2014), 446p. (in Russian)

    Google Scholar 

  7. Rahimov, A.B.: On an approach to solution to optimal control problems on the classes of piecewise constant, piecewise linear, and piecewise given functions. Tomsk State University J. Control Comput. Sci. 2(19), p20-30 (2012)

    Google Scholar 

  8. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  9. Yang, X.-S., He, X.: Swarm intelligence and evolutionary computation: overview and analysis. In: Yang, X.-S. (ed.) Recent Advances in Swarm Intelligence and Evolutionary Computation. SCI, vol. 585, pp. 1–23. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13826-8_1

    Chapter  Google Scholar 

  10. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  11. Raidl, G.R.: A unified view on hybrid metaheuristics. In: Almeida, F., et al. (eds.) HM 2006. LNCS, vol. 4030, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11890584_1

    Chapter  Google Scholar 

  12. Pham, D.T., et al.: The Bees Algorithm – A Novel Tool for Complex Optimisation Problems. In: Intelligent Production Machines and Systems - 2nd I*PROMS Virtual International Conference, 3–14 July 2006, pp. 454–459. Elsevier Ltd. (2006)

    Google Scholar 

  13. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  14. Šuster, P., Jadlovska, A.: Tracking trajectory of the mobile robot Khepera II using approaches of artificial intelligence. Acta Electrotechnica et Informatica 11(1), 38–43 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Konstantinov, S., Diveev, A. (2021). 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) Advances in Optimization and Applications. OPTIMA 2021. Communications in Computer and Information Science, vol 1514. Springer, Cham. https://doi.org/10.1007/978-3-030-92711-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92711-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92710-3

  • Online ISBN: 978-3-030-92711-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics