Unbiased, scalable sampling of closed kinematic chains

Yajia Zhang, Kris Hauser, Jingru Luo

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

Abstract

This paper presents a Monte Carlo technique for sampling configurations of a kinematic chain according to a specified probability density while accounting for loop closure constraints. A key contribution is a method for sampling sub-loops in unbiased fashion using analytical inverse kinematics techniques. Sub-loops are then iterated across the chain to produce samples for the entire chain. The method is demonstrated to scale well to high-dimensional chains (>200DOFs) and is applied to flexible 2D chains, protein molecules, and robots with multiple closed-chains.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Pages2459-2464
Number of pages6
DOIs
StatePublished - Nov 14 2013
Externally publishedYes
Event2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
Duration: May 6 2013May 10 2013

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2013 IEEE International Conference on Robotics and Automation, ICRA 2013
CountryGermany
CityKarlsruhe
Period5/6/135/10/13

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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  • Cite this

    Zhang, Y., Hauser, K., & Luo, J. (2013). Unbiased, scalable sampling of closed kinematic chains. In 2013 IEEE International Conference on Robotics and Automation, ICRA 2013 (pp. 2459-2464). [6630911] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ICRA.2013.6630911