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Publicação
Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case study
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Indoor Localization Machine Learning RobotAtFactory 4.0 Robotics Competitions Embedded systems
Contexto Educativo
Citação
Klein, Luan C.; Braun, João; Martins, Felipe N.; Wörtche, Heinrich; Oliveira, Andre Schneider; Mendes, João; Pinto, Vítor H.; Costa, Paulo (2023). Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case study. In IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). Tomar. p. p. 69-74. ISBN 979-835030121-2
Editora
IEEE