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

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 o...

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Bibliographic Details
Main Authors: Klein, Luan C., Braun, Joao, Martins, Felipe N., Wortche, Heinrich, de Oliveira, Andre Schneider, Mendes, Joao, Pinto, Vitor H., Costa, Paulo, Lima, Jose
Format: Conference Proceeding
Language:English
Subjects:
Decision making
Embedded systems
Indoor Localization
Location awareness
Machine Learning
Machine learning algorithms
Navigation
RobotAtFactory 4.0
Robotics Competitions
Training
Online Access:Get full text
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Summary: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.
ISSN:2573-9387
DOI:10.1109/ICARSC58346.2023.10129619