Advertisement

Mechanical PSO Aided by Extremum Seeking for Swarm Robots Cooperative Search

  • Qirong Tang
  • Peter Eberhard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

This paper addresses the issue of swarm robots cooperative search. A swarm intelligence based algorithm, mechanical Particle Swarm Optimization (PSO), is first conducted which takes into account the robot mechanical properties and guiding the robots searching for a target. In order to avoid the robot localization and to avoid noise due to feedback and measurements, a new scheme which uses Extremum Seeking (ES) to aid mechanical PSO is designed. The ES based method is capable of driving robots to the purposed states generated by mechanical PSO without the necessity of robot localization. By this way, the whole robot swarm approaches the searched target cooperatively. This pilot study is verified by numerical experiments in which different robot sensors are mimicked.

Keywords

Swarm Robotics Mechanical Particle Swarm Optimization Extremum Seeking Perturbation Cooperative Search 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ariyur, K.B., Krstić, M.: Real-Time Optimization by Extremum-Seeking Control. Wiley Interscience, Hoboken (2003)zbMATHCrossRefGoogle Scholar
  2. 2.
    Dürr, H.B., Stanković, M.S., Ebenbauer, C., Johansson, K.H.: Lie bracket approximation of extremum seeking systems. Automatica (2012) (accepted for publication)Google Scholar
  3. 3.
    Ghods, N., Krstić, M.: Multiagent deployment over a source. IEEE Transactions on Control Systems Technology 20(1), 277–285 (2012)Google Scholar
  4. 4.
    Hong, C., Li, K.: Swarm intelligence-based extremum seeking control. Expert Systems with Applications 38(12), 14852–14860 (2011)CrossRefGoogle Scholar
  5. 5.
    Stanković, M.S., Johansson, K.H., Stipanović, D.M.: Distributed seeking of Nash equilibria with applications to mobile sensor networks. IEEE Transactions on Automatic Control 57(4), 904–919 (2012)CrossRefGoogle Scholar
  6. 6.
    Tang, Q.: Cooperative Search by Mixed Simulated and Real Robots in a Swarm Based on Mechanical Particle Swarm Optimization. Doctoral thesis, University of Stuttgart, No. 25. Aachen: Shaker Verlag (2012)Google Scholar
  7. 7.
    Zhang, C., Ordóñez, R.: Extremum-Seeking Control and Applications: A Numerical Optimization-Based Approach. Springer, London (2011)Google Scholar
  8. 8.
    Zhang, C., Siranosian, A., Krstić, M.: Extremum seeking for moderately unstable systems and for autonomous vehicle target tracking without position measurements. Automatica 43(10), 1832–1839 (2007)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qirong Tang
    • 1
  • Peter Eberhard
    • 1
  1. 1.Institute of Engineering and Computational MechanicsUniversity of StuttgartStuttgartGermany

Personalised recommendations