@inproceedings{62a6533069c644d387b3ded8642283b4,
title = "Hybrid penetration depth computation using local projection and machine learning",
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.",
author = "Yeojin Kim and Dinesh Manocha and Kim, {Young J.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; null ; Conference date: 28-09-2015 Through 02-10-2015",
year = "2015",
month = dec,
day = "11",
doi = "10.1109/IROS.2015.7354052",
language = "English",
series = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4804--4809",
booktitle = "IROS Hamburg 2015 - Conference Digest",
}