Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

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

doi:10.1109/ICARSC58346.2023.10129619

Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

Luan C. Klein, João Braun, Felipe N. Martins, Heinrich Wörtche, André Schneider de Oliveira, João Mendes, Vítor H. Pinto, Paulo Costa, José Lima

Sensors and Smart Systems

Onderzoeksoutput: Contribution to conference proceeding › Academic › peer review

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The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

Originele taal-2	English
Titel	IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Pagina's	69-74
Aantal pagina's	6
DOI's	https://doi.org/10.1109/ICARSC58346.2023.10129619
Status	Published - 26 apr. 2023

Keywords

embedded systems
machine learning

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10.1109/ICARSC58346.2023.10129619

ICARSC_2023_pre-printSubmitted manuscript, 8,13 MB

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Klein, L. C., Braun, J., Martins, F. N., Wörtche, H., Oliveira, A. S. D., Mendes, J., Pinto, V. H., Costa, P., & Lima, J. (2023). Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study. In IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (blz. 69-74) https://doi.org/10.1109/ICARSC58346.2023.10129619

@inproceedings{031ac401a57e4a4180c2570bbefd77b3,

title = "Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study",

abstract = "The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.",

keywords = "embedded systems, machine learning, embedded systems, machine learning",

author = "Klein, {Luan C.} and Jo{\~a}o Braun and Martins, {Felipe N.} and Heinrich W{\"o}rtche and Oliveira, {Andr{\'e} Schneider de} and Jo{\~a}o Mendes and Pinto, {V{\'i}tor H.} and Paulo Costa and Jos{\'e} Lima",

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Klein, LC, Braun, J, Martins, FN , Wörtche, H, Oliveira, ASD, Mendes, J, Pinto, VH, Costa, P & Lima, J 2023, Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study. in IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). blz. 69-74. https://doi.org/10.1109/ICARSC58346.2023.10129619

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AU - Lima, José

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2023/4/26

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N2 - The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

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Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

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