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

Chyon Hae Kim, Shimon Sugawara, Shigeki Sugano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
Pages747-754
Number of pages8
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012 - Osaka, Japan
Duration: 2012 Nov 292012 Dec 1

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Kim, C. H., Sugawara, S., & Sugano, S. (2012). Linear prediction based uniform state sampling for sampling based motion planning systems. In 2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012 (pp. 747-754). [6651603] (IEEE-RAS International Conference on Humanoid Robots). https://doi.org/10.1109/HUMANOIDS.2012.6651603