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

• Published in: Optimization, Learning Algorithms and Applications pp. 181 - 194
• Main Authors: Klein, Luan C., Mendes, João, Braun, João, Martins, Felipe N., de Oliveira, Andre Schneider, Costa, Paulo, Wörtche, Heinrich, Lima, José
• Format: Book Chapter
• Language: English
• Published: Cham Springer Nature Switzerland 2024
• Series: Communications in Computer and Information Science
• Subjects: CNN; Indoor Localization; Robotic Competition
• ISBN: 3031530357; 9783031530357
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-031-53036-4_13

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.