Linear prediction based uniform state sampling for sampling based motion planning systems

Chyon Hae Kim, Shimon Sugawara, Shigeki Sugano

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We discuss the optimality and computational efficiency of sampling based motion planning (SBMP), which calculates dynamically precise and approximately optimal state transitions using arbitrarily selected nonlinear control system models. To ensure high optimality and computational efficiency, SBMP requires an approximately uniform state sampling function, though the non-linearity of the system models does not allow a perfect function. We propose linear prediction-based uniform state sampling (LPUSS) that samples approximately uniform state points while ensuring a dynamically correct state transition profile with a small calculation cost. LPUSS samples a state by using the given non-linear control system model after determining the input values by using a local linear transition model. We developed a mechanical motion planning system using LPUSS, articulated body algorithm, and parallel computing techniques. To validate LPUSS, we conducted experiments on double, triple, and sixtuple inverted pendulum models. LPUSS showed better optimality and computational efficiency with the double and triple inverted pendulum models, compared with randomized kinodynamic planning (RKP), which is based on rapid random tree (RRT), and our previously proposed rapid semi-optimal motion-planning method in which state sampling is based on uniform inputs. In particular, compared with our previous method, LPUSS was respectively 130 times and 3,000 times faster on double and triple inverted pendulum models under the condition of the same optimality. LPUSS found an approximately optimal swing up motion for the sixtuple inverted pendulum model 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.

本文言語English
ホスト出版物のタイトル2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
ページ747-754
ページ数8
DOI
出版ステータスPublished - 2012 12月 1
イベント2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012 - Osaka, Japan
継続期間: 2012 11月 292012 12月 1

出版物シリーズ

名前IEEE-RAS International Conference on Humanoid Robots
ISSN(印刷版)2164-0572
ISSN(電子版)2164-0580

Conference

Conference2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
国/地域Japan
CityOsaka
Period12/11/2912/12/1

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ
  • 人間とコンピュータの相互作用
  • 電子工学および電気工学

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