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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3SPH8SH
Repositóriosid.inpe.br/mtc-m21c/2019/02.18.11.40   (acesso restrito)
Última Atualização2019:02.18.11.40.20 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/02.18.11.40.20
Última Atualização dos Metadados2020:01.06.11.42.10 (UTC) administrator
DOI10.1177/1729881419825941
ISSN1729-8806
Chave de CitaçãoVérasMedeGuim:2019:RaExRa
TítuloRapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles
Ano2019
MêsJan.
Data de Acesso30 abr. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2462 KiB
2. Contextualização
Autor1 Véras, Luiz Gustavo Diniz de Oliveira
2 Medeiros, Felipe L. L.
3 Guimarães, Lamartine N. F.
Grupo1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto de Estudos Avançados (IEAv)
3 Instituto de Estudos Avançados (IEAv)
Endereço de e-Mail do Autor1 luiizgustavo@gmail.com
RevistaInternational Journal of Advanced Robotic Systems
Volume16
Número1
Histórico (UTC)2019-02-18 11:41:00 :: simone -> administrator :: 2019
2020-01-06 11:42:10 :: administrator -> simone :: 2019
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChavePath planning
RRT*
sampling
convex vertices
Sukharev grids
ResumoThe 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.
ÁreaCOMP
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvoveras_rapidly.pdf
Grupo de Usuáriossimone
Visibilidadeshown
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F2PHGS
DivulgaçãoWEBSCI; PORTALCAPES; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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