Adaptive and relaxed visibility-based PRM

Tian-Ming Bu; Zhen-Jian Li; Zheng Sun

doi:10.1109/ROBIO.2005.246258

Adaptive and relaxed visibility-based PRM

Tian-Ming Bu^*, Zhen-Jian Li, Zheng Sun

^*Corresponding author for this work

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

In this paper we introduce a new sampler for robotic motion planning using the probabilistic roadmap approach. We improve the previously-introduced visibility-based sampler with two heuristics. First, we adopt a space decomposition approach can guide the sampler by identifying promising areas of the configuration space where milestones can be efficiently generated. While the original visibility-based sampler uses a very selective criterion to determine whether a sampled configuration point is to be accepted as a new milestone, we use a relaxed criterion that can significantly reduce the computational cost in the roadmap construction phase while increasing the size of the roadmap only mildly.

Original language	English
Title of host publication	2005 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO
Publisher	IEEE
Pages	174-179
Number of pages	6
Edition	1st
ISBN (Print)	0780393155, 9780780393158
DOIs	https://doi.org/10.1109/ROBIO.2005.246258
Publication status	Published - 5 Jul 2005
Externally published	Yes
Event	IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2005 - , Hong Kong Duration: 5 Jul 2005 → 9 Jul 2005 https://ieeexplore.ieee.org/xpl/conhome/11203/proceeding

Publication series

Name	IEEE International Conference on Robotics and Biomimetics
Publisher	IEEE

Conference

Conference	IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2005
Country/Territory	Hong Kong
Period	5/07/05 → 9/07/05
Internet address	https://ieeexplore.ieee.org/xpl/conhome/11203/proceeding

Scopus Subject Areas

General Engineering

Access to Document

10.1109/ROBIO.2005.246258

Cite this

@inproceedings{0602b707b34649bc8ca7bd5d1b5d61d1,

title = "Adaptive and relaxed visibility-based PRM",

abstract = "In this paper we introduce a new sampler for robotic motion planning using the probabilistic roadmap approach. We improve the previously-introduced visibility-based sampler with two heuristics. First, we adopt a space decomposition approach can guide the sampler by identifying promising areas of the configuration space where milestones can be efficiently generated. While the original visibility-based sampler uses a very selective criterion to determine whether a sampled configuration point is to be accepted as a new milestone, we use a relaxed criterion that can significantly reduce the computational cost in the roadmap construction phase while increasing the size of the roadmap only mildly.",

author = "Tian-Ming Bu and Zhen-Jian Li and Zheng Sun",

year = "2005",

month = jul,

day = "5",

doi = "10.1109/ROBIO.2005.246258",

language = "English",

isbn = "0780393155",

series = "IEEE International Conference on Robotics and Biomimetics",

publisher = "IEEE",

pages = "174--179",

booktitle = "2005 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO",

address = "United States",

edition = "1st",

note = "IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2005 ; Conference date: 05-07-2005 Through 09-07-2005",

url = "https://ieeexplore.ieee.org/xpl/conhome/11203/proceeding",

}

Bu, T-M, Li, Z-J & Sun, Z 2005, Adaptive and relaxed visibility-based PRM. in 2005 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO. 1st edn, IEEE International Conference on Robotics and Biomimetics, IEEE, pp. 174-179, IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2005, Hong Kong, 5/07/05. https://doi.org/10.1109/ROBIO.2005.246258

TY - GEN

T1 - Adaptive and relaxed visibility-based PRM

AU - Bu, Tian-Ming

AU - Li, Zhen-Jian

AU - Sun, Zheng

PY - 2005/7/5

Y1 - 2005/7/5

N2 - In this paper we introduce a new sampler for robotic motion planning using the probabilistic roadmap approach. We improve the previously-introduced visibility-based sampler with two heuristics. First, we adopt a space decomposition approach can guide the sampler by identifying promising areas of the configuration space where milestones can be efficiently generated. While the original visibility-based sampler uses a very selective criterion to determine whether a sampled configuration point is to be accepted as a new milestone, we use a relaxed criterion that can significantly reduce the computational cost in the roadmap construction phase while increasing the size of the roadmap only mildly.

AB - In this paper we introduce a new sampler for robotic motion planning using the probabilistic roadmap approach. We improve the previously-introduced visibility-based sampler with two heuristics. First, we adopt a space decomposition approach can guide the sampler by identifying promising areas of the configuration space where milestones can be efficiently generated. While the original visibility-based sampler uses a very selective criterion to determine whether a sampled configuration point is to be accepted as a new milestone, we use a relaxed criterion that can significantly reduce the computational cost in the roadmap construction phase while increasing the size of the roadmap only mildly.

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U2 - 10.1109/ROBIO.2005.246258

DO - 10.1109/ROBIO.2005.246258

M3 - Conference proceeding

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SN - 0780393155

SN - 9780780393158

T3 - IEEE International Conference on Robotics and Biomimetics

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EP - 179

BT - 2005 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO

PB - IEEE

T2 - IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2005

Y2 - 5 July 2005 through 9 July 2005

ER -

Adaptive and relaxed visibility-based PRM

Abstract

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