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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Outdoor localization system for mobile robots based on radio-frequency signal strength

Maidana, Renan Guedes 02 March 2018 (has links)
Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-06-07T11:44:28Z No. of bitstreams: 1 RENAN_GUEDES_MAIDANA_DIS.pdf: 4462325 bytes, checksum: 589fff5df748f66fa3f6b644cbc058db (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-15T14:20:14Z (GMT) No. of bitstreams: 1 RENAN_GUEDES_MAIDANA_DIS.pdf: 4462325 bytes, checksum: 589fff5df748f66fa3f6b644cbc058db (MD5) / Made available in DSpace on 2018-06-15T14:58:29Z (GMT). No. of bitstreams: 1 RENAN_GUEDES_MAIDANA_DIS.pdf: 4462325 bytes, checksum: 589fff5df748f66fa3f6b644cbc058db (MD5) Previous issue date: 2018-03-02 / Na ?rea da Rob?tica M?vel, o problema da localiza??o ? definido como a determina??o da posi??o e orienta??o de um rob? em um espa?o tri-dimensional atrav?s de informa??es de seus sensores. A solu??o mais comum para esse problema ? utilizar um receptor de GPS (doingl?s, Global Positioning System), que reporta posi??o absoluta com rela??o a um sistema de coordenadas fixo e centralizado na Terra. Por?m, o sinal de GPS ? muito afetado por condi??es ambientais e oclus?o de linha de vis?o, por vezes fornecendo estimativas de posi??o de baixa qualidade, se houverem .Com inspira??o nestes problemas, este projeto prop?e um sistema de localiza??o para ser usado por um rob? terrestre em um ambiente externo n?o-controlado, onde h? indisponibilidade de GPS ou que suas medidas s?o de baixa qualidade. Tendo em vista que sensores de baixo custo apresentam medi??es imprecisas devido a fatores ambientais (e.g. terreno acidentado), ? proposta a utiliza??o de pares receptor-transmissor de R?dio-Frequ?ncia, onde a medida do Indicador de Pot?ncia de Sinal Recebido ? usada para estimar as dist?ncias entre receptor e trans- missor, que s?o por sua vez usadas para posicionamento. Essa medida possuia vantagem de ser independente da ilumina??o do ambiente e do estado do terreno, que afetam outros m?todos de localiza??o como Odometria Visual ou por rodas. Um erro m?dio de posiciona- mento de 0.41m foi alcan?ado atrav?s da fus?o de odometria por rodas, velocidade angular de um girosc?pio e pot?ncia de sinal recebido, em um algoritmo de Filtro de Kalman Esten- dido Aumentado, comum a melhoria de 82.66% referente ao erro m?dio de 2.38 m obtido com um sensor GPS comum. / In the field of Mobile Robotics, the localization problem consists on determining a robot?s position and orientation in a three-dimensional space through sensor information. The most common solution to this problem is to employ a Global Positioning System receiver, also known as GPS, which reports absolute position in relation to an Earth-centered fixed coordinate system. However, GPS signals are greatly affected by atmospheric conditions and line-of-sight occlusion, sometimes providing very poor position estimates, if any at all. Inspired by these problems, this project proposes a localization system to be used by a robot in an uncontrolled outdoor environment, where GPS measurements are poor or unavailable. As common sensors provide inaccurate position estimates due to environmental factors (e.g. rough terrain), we propose the use of Radio-Frequency receiver-transmitter pairs, in which the Received Signal Strength Indicator is used for estimating the distances between receiver and transmitter, which in turn are used for positioning. This measurement has the advantage of being independent from lighting conditions or the state of the terrain, factors which affect other localization methods such as visual or wheel odometry. A mean positioning error of 0.41 m was achieved by fusing wheel odometry, angular velocity from a gyroscope and the received signal strength, in an Augmented Extended Kalman Filter algorithm, with an improvement of 82.66% relative to the mean error of 2.38 m obtained with a common GPS sensor.

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