A GPU parallel computing method for LPUSS

Chyon Hae Kim, Shigeki Sugano

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We discuss the effective implementation of parallel processing for linear prediction-based uniform state sampling (LPUSS). In previous work, we proposed LPUSS as an optimization algorithm for mechanical motions that assures high optimality of the solutions and computational efficiency. In parallel computation, LPUSS requires balanced memory allocation and managed processing timing. In this paper, we propose an effective parallel computing method that assures high optimality and calculation efficiency in parallel processing using GPU processor. We conducted two experiments to validate the proposed method. In the first experiment, we compared single-thread processing for LPUSS and the proposed parallel processing. As a result of this experiment, calculation speed of LPUSS was about 4-20 times faster than that with single-thread CPU. In the second experiment, we applied the proposed method to the optimization of sixtuple inverted pendulum. As a result, the proposed method optimized the motion within 40 minutes. According to our survey, there is no other optimization method that is applicable to higher than quadruple inverted pendulum models with standard constraints.

Original languageEnglish
Pages (from-to)1199-1207
Number of pages9
JournalAdvanced Robotics
Volume27
Issue number15
DOIs
Publication statusPublished - 2013 Jul 1

Keywords

  • Dynamics
  • GPU
  • LPUSS
  • Motion planning
  • Parallel computing

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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