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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Samenvatting

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

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SDG 03 – Goede gezondheid en welzijn
SDG 09 – Industrie, innovatie en infrastructuur

Keywords

embedded systems
machine learning

Research Focus Areas Hanze University of Applied Sciences

Healthy Ageing

Research Focus Areas Research Centre or Centre of Expertise

Artificial Intelligence
Cyberfysical systems

Publinova thema's

ICT & Media
Gezondheid
Techniek

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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,

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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.",

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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 - Pinto, Vítor H.

AU - Costa, Paulo

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.

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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

Samenvatting

Duurzame ontwikkelingsdoelstellingen van de VN

Keywords

Research Focus Areas Hanze University of Applied Sciences

Research Focus Areas Research Centre or Centre of Expertise

Publinova thema's

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