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

• Ano de defesa: 2018
• Autor(a) principal: Maidana, Renan Guedes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul; Escola Politécnica; Brasil; PUCRS; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Robot Localization; Robot Perception; Wireless Sensor Networks; Radio-Frequency; Localização Robótica; Percepção Robótica; Redes de Sensores Wireless; Rádio-Frequência; CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/8140

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.