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

Open access

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

Open access

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.

OrganisatieHanze
Gepubliceerd inIEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) Pagina's: 69-74
Datum2023-04-26
TypeConferentiebijdrage
DOI10.1109/ICARSC58346.2023.10129619
TaalEngels

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