Detalhes bibliográficos
| Resumo: | The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model. |
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| Autores principais: | Klein, Luan C. |
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| Outros Autores: | Braun, João; Martins, Felipe N.; Wörtche, Heinrich; Oliveira, Andre Schneider; Mendes, João; Pinto, Vítor H.; Costa, Paulo Gomes da |
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| Assunto: | Indoor Localization Machine Learning RobotAtFactory 4.0 Robotics Competitions Embedded systems |
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| Ano: | 2023 |
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| País: | Portugal |
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| Tipo de documento: | comunicação em conferência |
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| Tipo de acesso: | acesso aberto |
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| Instituição associada: | Instituto Politécnico de Bragança |
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| Idioma: | inglês |
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| Origem: | Biblioteca Digital do IPB |
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