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

Research output: Chapter in Book/Report/Conference proceedingContribution to conference proceedingAcademicpeer-review

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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.
Original languageEnglish
Title of host publicationIEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Pages69-74
Number of pages6
DOIs
Publication statusPublished - 26 Apr 2023

Keywords

  • machine learning
  • embedded systems

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