Unbiased, scalable sampling of closed kinematic chains

Published

Conference Paper

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. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Zhang, Y; Hauser, K; Luo, J

Published Date

  • November 14, 2013

Published In

Start / End Page

  • 2459 - 2464

International Standard Serial Number (ISSN)

  • 1050-4729

International Standard Book Number 13 (ISBN-13)

  • 9781467356411

Digital Object Identifier (DOI)

  • 10.1109/ICRA.2013.6630911

Citation Source

  • Scopus