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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3SPH8SH
Repositorysid.inpe.br/mtc-m21c/2019/02.18.11.40   (restricted access)
Last Update2019:02.18.11.40.20 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m21c/2019/02.18.11.40.20
Metadata Last Update2020:01.06.11.42.10 (UTC) administrator
DOI10.1177/1729881419825941
ISSN1729-8806
Citation KeyVérasMedeGuim:2019:RaExRa
TitleRapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles
Year2019
MonthJan.
Access Date2024, May 08
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2462 KiB
2. Context
Author1 Véras, Luiz Gustavo Diniz de Oliveira
2 Medeiros, Felipe L. L.
3 Guimarães, Lamartine N. F.
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto de Estudos Avançados (IEAv)
3 Instituto de Estudos Avançados (IEAv)
Author e-Mail Address1 luiizgustavo@gmail.com
JournalInternational Journal of Advanced Robotic Systems
Volume16
Number1
History (UTC)2019-02-18 11:41:00 :: simone -> administrator :: 2019
2020-01-06 11:42:10 :: administrator -> simone :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsPath planning
RRT*
sampling
convex vertices
Sukharev grids
AbstractThe path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Rapidly exploring Random...
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4. Conditions of access and use
Languageen
Target Fileveras_rapidly.pdf
User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F2PHGS
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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