Using machine learning approaches to localization in an embedded system on RobotAtFactory 4.0 competition: a case study

• Main Author: Klein, Luan C.
• Publication Date: 2013
• Other Authors: Braun, João, Martins, Felipe N., Wörtche, Heinrich, Oliveira, Andre Schneider, Mendes, João, Pinto, Vítor H., Costa, Paulo Gomes da
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10198/7916
• topic: Indoor Localization; Machine Learning; RobotAtFactory 4.0; Robotics Competitions; Embedded systems

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.