Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Klein, LC (Autor) Outra; Mendes, J (Autor) Outra; Braun, J (Autor) Outra; Martins, FN (Autor) Outra; de Oliveira, AS (Autor) Outra; Paulo Gomes da Costa (Autor) FEUP; Wörtche, H (Autor) Outra; Lima, J (Autor) Outra

Ata de Conferência Internacional

Título: OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, PT II, OL2A 2023

Páginas: 181-194

3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A)

Ponta Delgada, PORTUGAL, SEP 27-29, 2023

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-010-2VV

DOI: 10.1007/978-3-031-53036-4_13

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14