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

Luan C Klein1,2, João Braun2,3,4,5, João Mendes2,3,6

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This summary is machine-generated.

This study introduces a machine learning approach for mobile robot localization using Random Forest Regressor. It achieves millimeter-scale accuracy in the RobotAtFactory 4.0 competition, rivaling analytical methods without needing exact marker positions.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Mobile robot localization is essential for navigation and mission completion.
  • Traditional localization methods rely on model calculations.
  • Artificial intelligence offers a promising alternative for robot localization.

Purpose of the Study:

  • To propose a machine learning-based approach for robot localization in the RobotAtFactory 4.0 competition.
  • To estimate robot pose using relative camera pose with respect to fiducial markers.
  • To validate the proposed method in a simulated environment.

Main Methods:

  • Utilizing an onboard camera to detect fiducial markers (ArUcos).
  • Estimating the relative pose between the camera and the markers.
  • Employing machine learning algorithms, specifically Random Forest Regressor, for robot pose estimation.
  • Validating the approach through simulation.

Main Results:

  • The Random Forest Regressor achieved the best performance among tested algorithms.
  • The proposed machine learning approach demonstrated millimeter-scale localization error.
  • The solution's accuracy is comparable to analytical approaches.

Conclusions:

  • Machine learning, particularly Random Forest Regressor, is a viable and effective method for robot localization.
  • The proposed approach offers an advantage by not requiring explicit knowledge of fiducial marker positions.
  • This AI-driven localization method shows high potential for real-world robotic applications.