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

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

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

Conference proceedings International

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

Pages: 181-194

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

Ponta Delgada, PORTUGAL, SEP 27-29, 2023

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Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14