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

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Klein, LC (Author) Other; Braun, J (Author) Other; Martins, FN (Author) Other; Wortche, H (Author) Other; de Oliveira, AS (Author) Other; Mendes, J (Author) Other; Pinto, VH (Author) FEUP; Paulo Gomes da Costa (Author) FEUP; Lima, J (Author) Other

Conference proceedings International

Title: 2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Pages: 69-74

IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Tomar, PORTUGAL, APR 26-27, 2023

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00Y-DQT

DOI: 10.1109/icarsc58346.2023.10129619

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6