Poisson-RRT

Chonhyon Park; Jia Pan; Dinesh Manocha

doi:10.1109/ICRA.2014.6907541

Poisson-RRT

Chonhyon Park, Jia Pan, Dinesh Manocha

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 Citations (Scopus)

Abstract

We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.

Original language	English
Title of host publication	Proceedings - 2014 IEEE International Conference on Robotics and Automation (ICRA)
Publisher	IEEE
Pages	4667-4673
ISBN (Electronic)	9781479936854
ISBN (Print)	9781479936861
DOIs	https://doi.org/10.1109/ICRA.2014.6907541
Publication status	Published - Jun 2014
Externally published	Yes
Event	2014 IEEE International Conference on Robotics and Automation (ICRA 2014) - Hong Kong Convention and Exhibition Centre, Hong Kong, China Duration: 31 May 2014 → 7 Jun 2014 http://www.icra2014.com/

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2014
ISSN (Print)	1050-4729

Conference

Conference	2014 IEEE International Conference on Robotics and Automation (ICRA 2014)
Abbreviated title	ICRA 2014
Place	China
City	Hong Kong
Period	31/05/14 → 7/06/14
Internet address	http://www.icra2014.com/

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10.1109/ICRA.2014.6907541

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@inproceedings{74e72fcbbaab4d1fb8d2f3287b345d2b,

title = "Poisson-RRT",

abstract = "We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.",

author = "Chonhyon Park and Jia Pan and Dinesh Manocha",

year = "2014",

month = jun,

doi = "10.1109/ICRA.2014.6907541",

language = "English",

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series = "Proceedings - IEEE International Conference on Robotics and Automation",

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pages = "4667--4673",

booktitle = "Proceedings - 2014 IEEE International Conference on Robotics and Automation (ICRA)",

address = "United States",

note = "2014 IEEE International Conference on Robotics and Automation (ICRA 2014), ICRA 2014 ; Conference date: 31-05-2014 Through 07-06-2014",

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Park, C, Pan, J & Manocha, D 2014, Poisson-RRT. in Proceedings - 2014 IEEE International Conference on Robotics and Automation (ICRA). Proceedings - IEEE International Conference on Robotics and Automation, vol. 2014, IEEE, pp. 4667-4673, 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong, China, 31/05/14. https://doi.org/10.1109/ICRA.2014.6907541

Poisson-RRT. / Park, Chonhyon; Pan, Jia; Manocha, Dinesh.
Proceedings - 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2014. p. 4667-4673 (Proceedings - IEEE International Conference on Robotics and Automation; Vol. 2014).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

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AU - Pan, Jia

AU - Manocha, Dinesh

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N2 - We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.

AB - We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.

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M3 - RGC 32 - Refereed conference paper (with host publication)

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T3 - Proceedings - IEEE International Conference on Robotics and Automation

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BT - Proceedings - 2014 IEEE International Conference on Robotics and Automation (ICRA)

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T2 - 2014 IEEE International Conference on Robotics and Automation (ICRA 2014)

Y2 - 31 May 2014 through 7 June 2014

ER -

Poisson-RRT

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