Hybrid penetration depth computation using local projection and machine learning
- 관리정보기술 faculty
- 등재 SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2015
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000124265
- ISBN 9781479999941
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/IROS.2015.7354052
초록/요약
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.
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