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Previous issue date: 2015-12-07 / Este trabalho prop?e uma nova estrat?gia de navega??o aut?noma assistida por algoritmo gen?tico com planejamento din?mico para rob?s m?veis terrestres, chamada DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm). A estrat?gia foi aplicada em ambientes - tanto est?ticos, quanto din?micos - nos quais a localiza??o e o formato dos obst?culos n?o s?o previamente conhecidos. A cada evento de deslocamento, uma nova rota ? planejada atrav?s de um algoritmo que minimiza a dist?ncia entre o rob? e o objetivo e maximiza a dist?ncia em rela??o aos obst?culos. Utilizando um sensor de localiza??o espacial e um conjunto de sensores de dist?ncia, a estrat?gia de navega??o proposta foi capaz de planejar dinamicamente percursos ?timos livres de colis?o. Simula??es realizadas em diferentes ambientes demostraram que a t?cnica fornece um alto grau de flexibilidade e robustez. Para isso, foram aplicadas diversas varia??es de par?metros gen?ticos, tais como: taxa de cruzamento, tamanho da popula??o, dentre outros. Finalmente, os resultados das simula??es demonstram satisfatoriamente a efic?cia e robustez da t?cnica DPNA-GA, validando-a para aplica??es reais em rob?s m?veis terrestres. / This work proposes a new autonomous navigation strategy assisted by genetic algorithm
with dynamic planning for terrestrial mobile robots, called DPNA-GA (Dynamic Planning
Navigation Algorithm optimized with Genetic Algorithm). The strategy was applied in
environments - both static and dynamic - in which the location and shape of the obstacles is
not known in advance. In each shift event, a control algorithm minimizes the distance
between the robot and the object and maximizes the distance from the obstacles, rescheduling
the route. Using a spatial location sensor and a set of distance sensors, the proposed
navigation strategy is able to dynamically plan optimal collision-free paths. Simulations
performed in different environments demonstrated that the technique provides a high degree
of flexibility and robustness. For this, there were applied several variations of genetic
parameters such as: crossing rate, population size, among others. Finally, the simulation
results successfully demonstrate the effectiveness and robustness of DPNA-GA technique,
validating it for real applications in terrestrial mobile robots.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/21173 |
| Date | 07 December 2015 |
| Creators | Oliveira, ?tila Varela Ferreira Medeiros de |
| Contributors | 02099790469, http://lattes.cnpq.br/3475337353676349, D?ria Neto, Adri?o Duarte, 10749896434, http://lattes.cnpq.br/1987295209521433, Pedrosa, Diogo Pinheiro Fernandes, 02199024458, http://lattes.cnpq.br/3276436982330644, Lima J?nior, Francisco Chagas de, 75046105420, http://lattes.cnpq.br/9342041276186254, Fernandes, Marcelo Augusto Costa |
| Publisher | Universidade Federal do Rio Grande do Norte, PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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