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Swarm Intelligence for Collective Robotic Search

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Design and Control of Intelligent Robotic Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 177))

Abstract

This chapter presents three strategies for the navigation of a swarm of robots for a target search application in a hazardous environment. The strategies explored include greedy search and two computational intelligence techniques—particle swarm optimization and fuzzy logic. Results for the collective search are presented for simulated environments containing single and multiple targets, with and without obstacles. The proposed navigation strategies can be further developed and applied to real-world applications such as aiding in disaster recovery, detection of hazardous materials, and many other high-risk tasks.

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Author information

Authors and Affiliations

  1. Real-Time Power and Intelligent Systems Laboratory, Missouri University of Science and Technology, Rolla, MO 65409, USA

    Lisa L. Grant & Ganesh K. Venayagamoorthy

Authors
  1. Lisa L. Grant
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  2. Ganesh K. Venayagamoorthy
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Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Dikai Liu

  2. Texas A&M University, College Station, TX, USA

    Lingfeng Wang

  3. National University of Singapore, Singapore

    Kay Chen Tan

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© 2009 Springer-Verlag Berlin Heidelberg

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Grant, L.L., Venayagamoorthy, G.K. (2009). Swarm Intelligence for Collective Robotic Search. In: Liu, D., Wang, L., Tan, K.C. (eds) Design and Control of Intelligent Robotic Systems. Studies in Computational Intelligence, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89933-4_2

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  • DOI: https://doi.org/10.1007/978-3-540-89933-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89932-7

  • Online ISBN: 978-3-540-89933-4

  • eBook Packages: EngineeringEngineering (R0)

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