A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Luan Carlos Klein; João Braun; João Mendes; Vítor H. Pinto; Felipe Martins; André Schneider de Oliveira; Heinrich Wörtche; Paulo Gomes Costa; José Lima

doi:10.3390/s23063128

A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Luan Carlos Klein (First author)
, João Braun
, João Mendes
, Vítor H. Pinto
, Felipe Martins
, André Schneider de Oliveira
, Heinrich Wörtche
, Paulo Gomes Costa
, José Lima

Sensors and Smart Systems

The Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Braganca
INESC Technology and Science
Universidade Tecnológica Federal do Paraná (UTFPR), Department of Electronics (DAELN)
Instituto Politécnico de Bragança, Laboratório para a Sustentabilidade e Tecnologia em Regioes de Montanha (SusTEC)
University of Porto (FEUP), Faculty of Engineering
University of Minho, ALGORITMI Center
SYSTEC (DIGI2) Research Center for Systems and Technologies (Digital and Intelligent Industry Lab)
Eindhoven University of Technology, Department of Electrical Engineering

Onderzoeksoutput: Article › Academic › peer review

Samenvatting

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

Originele taal-2	English
Artikelnummer	3128
Tijdschrift	Sensors
Volume	23
Nummer van het tijdschrift	6
DOI's	https://doi.org/10.3390/s23063128 https://doi.org/10.3390/s23063128
Status	Published - 15 mrt. 2023

Duurzame ontwikkelingsdoelstellingen van de VN

Deze output draagt bij aan de volgende duurzame ontwikkelingsdoelstelling(en)

SDG 03 – Goede gezondheid en welzijn
SDG 09 – Industrie, innovatie en infrastructuur

Keywords

indoor lokalisatie
industrie 4.0
robotica wedstrijden
vaste merktekens

Research Focus Areas Hanze University of Applied Sciences

Healthy Ageing

Research Focus Areas Research Centre or Centre of Expertise

Artificial Intelligence
Cyberfysical systems

Publinova thema's

ICT & Media
Gezondheid
Techniek

Toegang tot document

10.3390/s23063128Licentie: CC BY
10.3390/s23063128

Hanze Impact Event
Wörtche, H. (Speaker) & Manting, F. (Speaker)
31 mei 2023
Activiteit: Oral presentation

Citeer dit

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abstract = "Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.",

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AU - Schneider de Oliveira, André

AU - Wörtche, Heinrich

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A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

Samenvatting

Duurzame ontwikkelingsdoelstellingen van de VN

Keywords

Research Focus Areas Hanze University of Applied Sciences

Research Focus Areas Research Centre or Centre of Expertise

Publinova thema's

Toegang tot document

Vingerafdruk

Activiteiten

Hanze Impact Event

Citeer dit