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

Luan C. Klein; João Mendes; João Braun; Felipe N. Martins; Andre Schneider de Oliveira; Paulo Costa; Heinrich Wörtche; José Lima

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

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

Luan C. Klein (Corresponding author)
, João Mendes
, João Braun
, Felipe N. Martins
, Andre Schneider de Oliveira
, Paulo Costa
, Heinrich Wörtche
, José Lima

Integrated Circuits

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

28 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	Optimization, Learning Algorithms and Applications
Subtitle of host publication	Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part II
Editors	Ani I. Pereira, Armando Mendes, Florabela P. Fernandes, Maria F. Pacheco, João.P. Coelho, José Lima
Place of Publication	Cham
Publisher	Springer
Pages	181-194
Number of pages	14
ISBN (Electronic)	978-3-031-53036-4
ISBN (Print)	978-3-031-53035-7
DOIs	https://doi.org/10.1007/978-3-031-53036-4_13
Publication status	Published - 3 Feb 2024
Event	3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023 - Ponta Delgada, Portugal Duration: 27 Sept 2023 → 29 Sept 2023

Publication series

Name	Communications in Computer and Information Science (CCIS)
Volume	1982
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023
Country/Territory	Portugal
City	Ponta Delgada
Period	27/09/23 → 29/09/23

Keywords

CNN
Indoor Localization
Robotic Competition

Access to Document

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

pdfFinal published version, 1.18 MBLicence: CC BY

Cite this

Klein, L. C., Mendes, J., Braun, J., Martins, F. N., Schneider de Oliveira, A., Costa, P., Wörtche, H., & Lima, J. (2024). Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition. In A. I. Pereira, A. Mendes, F. P. Fernandes, M. F. Pacheco, J. P. Coelho, & J. Lima (Eds.), Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part II (pp. 181-194). (Communications in Computer and Information Science (CCIS); Vol. 1982). Springer. https://doi.org/10.1007/978-3-031-53036-4_13

Klein, Luan C. ; Mendes, João ; Braun, João et al. / Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition. Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part II. editor / Ani I. Pereira ; Armando Mendes ; Florabela P. Fernandes ; Maria F. Pacheco ; João.P. Coelho ; José Lima. Cham : Springer, 2024. pp. 181-194 (Communications in Computer and Information Science (CCIS)).

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abstract = "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.",

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Klein, LC, Mendes, J, Braun, J, Martins, FN, Schneider de Oliveira, A, Costa, P, Wörtche, H & Lima, J 2024, Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition. in AI Pereira, A Mendes, FP Fernandes, MF Pacheco, JP Coelho & J Lima (eds), Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part II. Communications in Computer and Information Science (CCIS), vol. 1982, Springer, Cham, pp. 181-194, 3rd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2023, Ponta Delgada, Portugal, 27/09/23. https://doi.org/10.1007/978-3-031-53036-4_13

Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition. / Klein, Luan C. (Corresponding author); Mendes, João; Braun, João et al.
Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part II. ed. / Ani I. Pereira; Armando Mendes; Florabela P. Fernandes; Maria F. Pacheco; João.P. Coelho; José Lima. Cham: Springer, 2024. p. 181-194 (Communications in Computer and Information Science (CCIS); Vol. 1982).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Klein LC, Mendes J, Braun J, Martins FN, Schneider de Oliveira A, Costa P et al. Deep Learning-Based Localization Approach for Autonomous Robots in the RobotAtFactory 4.0 Competition. In Pereira AI, Mendes A, Fernandes FP, Pacheco MF, Coelho JP, Lima J, editors, Optimization, Learning Algorithms and Applications: Third International Conference, OL2A 2023, Ponta Delgada, Portugal, September 27–29, 2023, Revised Selected Papers, Part II. Cham: Springer. 2024. p. 181-194. (Communications in Computer and Information Science (CCIS)). doi: 10.1007/978-3-031-53036-4_13