Parallel evolutionary artificial potential field for path planning—an implementation on GPU

Ulises Orozco-Rosas; Oscar Montiel; Roberto Sepúlveda

doi:10.1007/978-3-319-17747-2_25

Parallel evolutionary artificial potential field for path planning—an implementation on GPU

Ulises Orozco-Rosas, Oscar Montiel, Roberto Sepúlveda

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English
Pages (from-to)	319-332
Number of pages	14
Journal	Studies in Computational Intelligence
Volume	601
DOIs	https://doi.org/10.1007/978-3-319-17747-2_25
State	Published - 2015
Externally published	Yes

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10.1007/978-3-319-17747-2_25

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title = "Parallel evolutionary artificial potential field for path planning—an implementation on GPU",

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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AU - Montiel, Oscar

AU - Sepúlveda, Roberto

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PY - 2015

Y1 - 2015

N2 - 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.

AB - 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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U2 - 10.1007/978-3-319-17747-2_25

DO - 10.1007/978-3-319-17747-2_25

M3 - Artículo

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SN - 1860-949X

VL - 601

SP - 319

EP - 332

JO - Studies in Computational Intelligence

JF - Studies in Computational Intelligence

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Parallel evolutionary artificial potential field for path planning—an implementation on GPU

Abstract

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