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Parallel Evolutionary Artificial Potential Field for Path Planning—An Implementation on GPU

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Book cover Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 601))

Abstract

This work presents the implementation of the Parallel Evolutionary Artificial Potential Field (PEAPF) on a Graphics Processing Unit (GPU) as an improvement to speed up the path planning computation in mobile robot navigation. Simulation results to validate the analysis and implementation are provided; they were specifically made to show the effectiveness and the efficiency of the proposal.

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Acknowledgments

We thank to Instituto Politécnico Nacional (IPN), to the Comisión de Fomento y Apoyo Académico del IPN (COFAA), and the Mexican National Council of Science and Technology (CONACYT) for supporting our research activities.

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Authors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI-IPN), Av. Del Parque No. 1310, Mesa de Otay, 22510, Tijuana, B.C., México

    Ulises Orozco-Rosas, Oscar Montiel & Roberto Sepúlveda

Authors
  1. Ulises Orozco-Rosas
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  2. Oscar Montiel
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  3. Roberto Sepúlveda
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Corresponding author

Correspondence to Oscar Montiel .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Orozco-Rosas, U., Montiel, O., Sepúlveda, R. (2015). Parallel Evolutionary Artificial Potential Field for Path Planning—An Implementation on GPU. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_25

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  • DOI: https://doi.org/10.1007/978-3-319-17747-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17746-5

  • Online ISBN: 978-3-319-17747-2

  • eBook Packages: EngineeringEngineering (R0)

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