A new parameterized potential family for path planning algorithms
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da FURG (RI FURG) |
Texto Completo: | http://repositorio.furg.br/handle/1/1148 |
Resumo: | In this work, it is proposed a new family of potentials for path planning algorithms, one kind to the goal and other to the obstacles. With these new potentials it is possible to parameterize the potential scale length and strength easily, providing better control over the moving object path characteristics. In this way, the path problem can be treated analytically. For example, the minimum distance between the moving object and the obstacles can be calculated as a function of the potential parameters. Simulations are made to test its ability to guide a vehicle through an obstacle-free path towards the goal. The success rate of the moving object on reaching the goal is compared with the potential parameters and with obstacle configuration and distribution parameters. |
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Ferrari, Fabricio2011-10-28T12:54:57Z2011-10-28T12:54:57Z2009FERRARI, F.. A new parameterized potential family for path planning algorithms. International Journal on Artificial Intelligence Tools, v. 18, n. 6, p. 949-957, 2009. Disponível em: <http://www.ferrari.pro.br/home/publications/papers/FFerrari_IJAIT2009_00047.pdf>. Acesso em: 25 out. 2011.http://repositorio.furg.br/handle/1/1148In this work, it is proposed a new family of potentials for path planning algorithms, one kind to the goal and other to the obstacles. With these new potentials it is possible to parameterize the potential scale length and strength easily, providing better control over the moving object path characteristics. In this way, the path problem can be treated analytically. For example, the minimum distance between the moving object and the obstacles can be calculated as a function of the potential parameters. Simulations are made to test its ability to guide a vehicle through an obstacle-free path towards the goal. The success rate of the moving object on reaching the goal is compared with the potential parameters and with obstacle configuration and distribution parameters.engAutonomous navigationPotential theoryPath planningA new parameterized potential family for path planning algorithmsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FURG (RI FURG)instname:Universidade Federal do Rio Grande (FURG)instacron:FURGORIGINALA NEW PARAMETERIZED.pdfA NEW PARAMETERIZED.pdfapplication/pdf400317https://repositorio.furg.br/bitstream/1/1148/1/A%20NEW%20PARAMETERIZED.pdf57be5b54d903cc826111a6f6e6475ef8MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81724https://repositorio.furg.br/bitstream/1/1148/2/license.txt5b92b9704b4f13242d70e45ddef35a68MD52open access1/11482011-10-28 10:54:57.908open accessoai:repositorio.furg.br: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Repositório InstitucionalPUBhttps://repositorio.furg.br/oai/request || http://200.19.254.174/oai/requestopendoar:2011-10-28T12:54:57Repositório Institucional da FURG (RI FURG) - Universidade Federal do Rio Grande (FURG)false |
dc.title.pt_BR.fl_str_mv |
A new parameterized potential family for path planning algorithms |
title |
A new parameterized potential family for path planning algorithms |
spellingShingle |
A new parameterized potential family for path planning algorithms Ferrari, Fabricio Autonomous navigation Potential theory Path planning |
title_short |
A new parameterized potential family for path planning algorithms |
title_full |
A new parameterized potential family for path planning algorithms |
title_fullStr |
A new parameterized potential family for path planning algorithms |
title_full_unstemmed |
A new parameterized potential family for path planning algorithms |
title_sort |
A new parameterized potential family for path planning algorithms |
author |
Ferrari, Fabricio |
author_facet |
Ferrari, Fabricio |
author_role |
author |
dc.contributor.author.fl_str_mv |
Ferrari, Fabricio |
dc.subject.por.fl_str_mv |
Autonomous navigation Potential theory Path planning |
topic |
Autonomous navigation Potential theory Path planning |
description |
In this work, it is proposed a new family of potentials for path planning algorithms, one kind to the goal and other to the obstacles. With these new potentials it is possible to parameterize the potential scale length and strength easily, providing better control over the moving object path characteristics. In this way, the path problem can be treated analytically. For example, the minimum distance between the moving object and the obstacles can be calculated as a function of the potential parameters. Simulations are made to test its ability to guide a vehicle through an obstacle-free path towards the goal. The success rate of the moving object on reaching the goal is compared with the potential parameters and with obstacle configuration and distribution parameters. |
publishDate |
2009 |
dc.date.issued.fl_str_mv |
2009 |
dc.date.accessioned.fl_str_mv |
2011-10-28T12:54:57Z |
dc.date.available.fl_str_mv |
2011-10-28T12:54:57Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
FERRARI, F.. A new parameterized potential family for path planning algorithms. International Journal on Artificial Intelligence Tools, v. 18, n. 6, p. 949-957, 2009. Disponível em: <http://www.ferrari.pro.br/home/publications/papers/FFerrari_IJAIT2009_00047.pdf>. Acesso em: 25 out. 2011. |
dc.identifier.uri.fl_str_mv |
http://repositorio.furg.br/handle/1/1148 |
identifier_str_mv |
FERRARI, F.. A new parameterized potential family for path planning algorithms. International Journal on Artificial Intelligence Tools, v. 18, n. 6, p. 949-957, 2009. Disponível em: <http://www.ferrari.pro.br/home/publications/papers/FFerrari_IJAIT2009_00047.pdf>. Acesso em: 25 out. 2011. |
url |
http://repositorio.furg.br/handle/1/1148 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da FURG (RI FURG) instname:Universidade Federal do Rio Grande (FURG) instacron:FURG |
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Universidade Federal do Rio Grande (FURG) |
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FURG |
institution |
FURG |
reponame_str |
Repositório Institucional da FURG (RI FURG) |
collection |
Repositório Institucional da FURG (RI FURG) |
bitstream.url.fl_str_mv |
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Repositório Institucional da FURG (RI FURG) - Universidade Federal do Rio Grande (FURG) |
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