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Towards GPU-Accelerated PRM for Autonomous Navigation

  • Janelle BlankenburgEmail author
  • Richard Kelley
  • David Feil-Seifer
  • Rui Wu
  • Lee Barford
  • Frederick C. HarrisJr.
Conference paper
  • 49 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1134)

Abstract

Sampling based planning is an important step for long-range navigation for an autonomous vehicle. This work proposes a GPU-accelerated sampling based path planning algorithm which can be used as a global planner in autonomous navigation tasks. A modified version of the generation portion for the Probabilistic Road Map (PRM) algorithm is presented which reorders some steps of the algorithm in order to allow for parallelization and thus can benefit highly from utilization of a GPU. The GPU and CPU algorithms were compared using a simulated navigation environment with graph generation tasks of several different sizes. It was found that the GPU-accelerated version of the PRM algorithm had significant speedup over the CPU version (up to 78×). This results provides promising motivation towards implementation of a real-time autonomous navigation system in the future.

Keywords

Path planning Autonomous vehicle Probabilistic roadmap Parallel computing Speedup 

Notes

Acknowledgements

This material is based in part upon work supported by the National Science Foundation under grant numbers IIA-1301726 and IIS-1719027. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Janelle Blankenburg
    • 1
    Email author
  • Richard Kelley
    • 2
  • David Feil-Seifer
    • 1
  • Rui Wu
    • 3
  • Lee Barford
    • 4
    • 5
  • Frederick C. HarrisJr.
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Nevada, RenoRenoUSA
  2. 2.Nevada Center for Applied ResearchUniversity of Nevada, RenoRenoUSA
  3. 3.Department of Computer ScienceEast Carolina UniversityGreenvilleUSA
  4. 4.Department of Computer Science and EngineeringUniversity of Nevada, RenoRenoUSA
  5. 5.Keysight LaboratoriesKeysight TechnologiesRenoUSA

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