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Deep learning-based localization approach for autonomous robots in the robotAtFactory 4.0 competition
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Orientador(es)
Resumo(s)
Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.
Descrição
Palavras-chave
CNN Indoor localization Robotic competition
Contexto Educativo
Citação
Klein, Luan C.; Mendes, João; Braun, João; Martins, Felipe N.; Oliveira, Andre Schneider de; Costa, Paulo; Wörtche, Heinrich; Lima, José (2024). Deep learning-based localization approach for autonomous robots in the robotAtFactory 4.0 competition. Communications in Computer and Information Science. In Third International Conference, OL2A 2023. Ponta Delgada,Portugal. 1982, p. 181-194