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Abstract(s)
Muitos problemas do mundo real resolvidos nos dias de hoje envolvem a tomada de decisões e a realização de tarefas deveras complexas. Se a grande generalidade das decisões são tomadas pelo ser humano, em sentido oposto, as tarefas acabam por ser realizadas por sistemas robóticos que permitem trazer uma maior eficácia, segurança e até mesmo desempenho em comparação com o Homem. Na última década, foram feitas inúmeras pesquisas a respeito do controlo de trajetórias dos manipuladores robóticos. Paralelamente foram aplicados algoritmos genéticos em diversos campos na robótica de modo a ser possível obter uma optimização em várias áreas como controlo robótico, processamento de imagens, reconhecimento de padrões ou até mesmo reconhecimento da fala. Assim, é estudado a abordagem do planeamento de duas trajetórias ideais, com um braço robótico, recorrendo aos algoritmos genéticos. Como caso de estudo, os principais objetivos passam pela trajetória mais próxima possível da trajetória ideal, implicando o menor erro possível para realizar o movimento pretendido pelo manipulador robótico. No que toca à cinemática do manipulador, o braço apresenta dois graus de liberdade, sendo que se recorre à técnica Newton-Euler para calcular as suas equações dinâmicas e utiliza-se o programa MatLab, no qual se encontra embebido a plataforma Simulink para verificar a simulação do sistema criado.
Many real-world problems solved these days involve decision-making and performing complex tasks. If the great majority of decisions are made by the human being the tasks it selves end up being carried out by robotic systems that allow to bring greater efficiency, safety and even performance in comparison with the human kind. In the last decade, a lot of research has been done on the trajectory control of robotic manipulators. At the same time, genetic algorithms were applied in several fields in robotics to be able to obtain optimization in various areas such as robotic control, image processing, pattern recognition or even speech recognition. Thus, the approach to planning two ideal trajectories with a robotic arm is studied, resorting to genetic algorithms. As a case study, the main goal of this study is to get the trajectory as close as possible to the ideal one to get the minimum error possible to perform the movement intended by the robotic manipulator. Regarding the manipulator's kinematics, the arm has two degrees of freedom and using the Newton-Euler technique, it is possible to calculate its dynamics equations and to verify and test the simulation of the created system using MatLab, more concretely the Simulink platform.
Many real-world problems solved these days involve decision-making and performing complex tasks. If the great majority of decisions are made by the human being the tasks it selves end up being carried out by robotic systems that allow to bring greater efficiency, safety and even performance in comparison with the human kind. In the last decade, a lot of research has been done on the trajectory control of robotic manipulators. At the same time, genetic algorithms were applied in several fields in robotics to be able to obtain optimization in various areas such as robotic control, image processing, pattern recognition or even speech recognition. Thus, the approach to planning two ideal trajectories with a robotic arm is studied, resorting to genetic algorithms. As a case study, the main goal of this study is to get the trajectory as close as possible to the ideal one to get the minimum error possible to perform the movement intended by the robotic manipulator. Regarding the manipulator's kinematics, the arm has two degrees of freedom and using the Newton-Euler technique, it is possible to calculate its dynamics equations and to verify and test the simulation of the created system using MatLab, more concretely the Simulink platform.
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Keywords
Robótica Manipulador Braço Algoritmo Genético Optimização MatLab Simulink Robotics Manipulator Arm Genetic Algorithm Optimization