Advertisement

Vector Field Benchmark for Collective Search in Unknown Dynamic Environments

  • Palina BartashevichEmail author
  • Welf Knors
  • Sanaz Mostaghim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11172)

Abstract

This paper presents a Vector Field Benchmark (VFB) generator to study and evaluate the performance of collective search algorithms under the influence of unknown external dynamic environments. The VFB generator is inspired by nature (simulating wind or flow) and constructs artificially dynamic environments based on time-dependent vector fields with moving singularities (vortices). Some experiments using the Particle Swarm Optimization (PSO) algorithm, along with two specially developed updating mechanisms for the global knowledge about the external environment, are conducted to investigate the performance of the proposed benchmarks.

References

  1. 1.
    Atyabi, A., Phon-Amnuaisuk, S., Ho, C.K.: Navigating a robotic swarm in an uncharted 2D landscape. Appl. Soft Comput. 10(1), 149–169 (2010)CrossRefGoogle Scholar
  2. 2.
    Bartashevich, P., Grimaldi, L., Mostaghim, S.: PSO-based search mechanism in dynamic environments: swarms in vector fields. In: 2017 IEEE Congress on Evolutionary Computation, pp. 1263–1270 (2017)Google Scholar
  3. 3.
    Bartashevich, P., Koerte, D., Mostaghim, S.: Energy-saving decision making for aerial swarms: PSO-based navigation in vector fields. In: 2017 IEEE Symposium Series on Computational Intelligence, pp. 1–8 (2017)Google Scholar
  4. 4.
    Demazure, M.: Singular points of vector fields. In: Demazure, M. (ed.) Bifurcations and Catastrophes, pp. 219–247. Springer, Berlin (2000).  https://doi.org/10.1007/978-3-642-57134-3_9CrossRefzbMATHGoogle Scholar
  5. 5.
    Doctor, S., Venayagamoorthy, G.K., Gudise, V.G.: Optimal PSO for collective robotic search applications. In: Proceedings of the 2004 Congress on Evolutionary Computation, Vol. 2, pp. 1390–1395 (2004)Google Scholar
  6. 6.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Company, Cambridge (2004)zbMATHGoogle Scholar
  7. 7.
    Günther, T., Theisel, H.: The state of the art in vortex extraction. Computer Graphics Forum, To appear (2018)CrossRefGoogle Scholar
  8. 8.
    Hamann, H.: Scenarios of swarm robotics. In: Hamann, H. (ed.) Swarm Robotics: A Formal Approach, pp. 65–93. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74528-2_4CrossRefGoogle Scholar
  9. 9.
    Meng, Q.-H., Yang, W.-X., Wang, Y., Zeng, M.: Collective odor source estimation and search in time-variant airflow environments using mobile robots. Sensors 11(11), 10415–10443 (2011).  https://doi.org/10.3390/s111110415CrossRefGoogle Scholar
  10. 10.
    Helman, J., Hesselink, L.: Representation and display of vector field topology in fluid flow data sets. Computer 22(8), 27–36 (1989)CrossRefGoogle Scholar
  11. 11.
    Hereford, J.M., Siebold, M., Nichols, S.: Using the particle swarm optimization algorithm for robotic search applications. In: 2007 IEEE Swarm Intelligence Symposium, pp. 53–59 (2007)Google Scholar
  12. 12.
    Jatmiko, W., Sekiyama, K., Fukuda, T.: A mobile robots PSO-based for odor source localization in dynamic advection-diffusion environment. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (2006)Google Scholar
  13. 13.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Marin, R.D.C.: Vector field design notes (2008)Google Scholar
  15. 15.
    Rukundo, O., Hanqiang, C.: Nearest neighbor value interpolation. In: International Journal of Advanced Computer Science and Applications (2012)Google Scholar
  16. 16.
    Sheetal, Venayagamoorthy, G.K.: Unmanned vehicle navigation using swarm intelligence. In: Proceedings of International Conference on Intelligent Sensing and Information Processing (2004)Google Scholar
  17. 17.
    Wang, X., Yi, P., Hong, Y.: Dynamic optimization for multi-agent systems with external disturbances. Control. Theory Technol. 12(2), 132–138 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of MagdeburgMagdeburgGermany

Personalised recommendations