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Probabilistic Roadmaps and Hierarchical Genetic Algorithms for Optimal Motion Planning

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Intelligent Systems in Science and Information 2014 (SAI 2014)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 591))

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Abstract

In this paper we present a motion planning algorithm that combines between Probabilistic Roadmaps (PRM) and Hierarchical Genetic Algorithms (HGA) in order to generate optimal motions for a non holonomic mobile robot. PRM are used to generate a set of paths that will be optimized by HGA, the obtained trajectory leads a non holonomic mobile robot from an initial to a final configuration while maintaining feasibility and no-collision with obstacles.

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Author information

Authors and Affiliations

  1. LRPE Laboratory, USTHB University, Algiers, Algeria

    Abdelhalim Lakhdari & Nouara Achour

Authors
  1. Abdelhalim Lakhdari
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  2. Nouara Achour
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Corresponding author

Correspondence to Abdelhalim Lakhdari .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, London, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, London, United Kingdom

    Rahul Bhatia

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Lakhdari, A., Achour, N. (2015). Probabilistic Roadmaps and Hierarchical Genetic Algorithms for Optimal Motion Planning. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems in Science and Information 2014. SAI 2014. Studies in Computational Intelligence, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-14654-6_20

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  • DOI: https://doi.org/10.1007/978-3-319-14654-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14653-9

  • Online ISBN: 978-3-319-14654-6

  • eBook Packages: EngineeringEngineering (R0)

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