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

Onderzoeksoutput: Contribution to conference proceedingAcademicpeer 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-2English
TitelIEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Pagina's69-74
Aantal pagina's6
DOI's
StatusPublished - 26 apr. 2023

Keywords

  • machine learning
  • embedded systems

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