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Hybrid penetration depth computation using local projection and machine learning

Title
Hybrid penetration depth computation using local projection and machine learning
Authors
Kim Y.Manocha D.Kim Y.J.
Ewha Authors
김영준
SCOPUS Author ID
김영준scopus
Issue Date
2015
Journal Title
IEEE International Conference on Intelligent Robots and Systems
ISSN
2153-0858JCR Link
Citation
vol. 2015-December, pp. 4804 - 4809
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCOPUS scopus
Abstract
We present a new hybrid approach to computing penetration depth (PD) for general polygonal models. Our approach exploits both local and global approaches to PD computation and can compute error-bounded PD approximations for both deep and shallow penetrations. We use a two-step formulation: the first step corresponds to a global approximation approach that samples the configuration space with bounded error using support vector machines; the second step corresponds to a local optimization that performs a projection operation refining the penetration depth. We have implemented this hybrid algorithm on a standard PC platform and tested its performance with various benchmarks. The experimental results show that our algorithm offers significant benefits over previously developed local-only and global-only methods used to compute the PD. © 2015 IEEE.
DOI
10.1109/IROS.2015.7354052
ISBN
9781479999941
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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