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