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Estrat?gia de navega??o com planejamento din?mico e algoritmo gen?tico aplicada a rob?s m?veis terrestresOliveira, ?tila Varela Ferreira Medeiros de 07 December 2015 (has links)
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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.
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