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Mechatronic Device Control by Artificial Intelligence.

Martin Bohušík1, Vladimír Stenchlák1, Miroslav Císar1

  • 1Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) prediction was used to control motors in a spherical parallel kinematic structure, improving effector positioning accuracy. This method effectively calibrated mechanical inaccuracies for precise control of the device's optical axis.

Keywords:
agile eyeartificial intelligenceneural networksprediction

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Area of Science:

  • Robotics and Mechatronics
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Artificial intelligence (AI) is increasingly vital for innovation across diverse fields.
  • Parallel kinematic structures offer high dynamics and multiple degrees of freedom.
  • Mechatronic devices often face challenges with mechanical inaccuracies and clearances.

Purpose of the Study:

  • To investigate the application of AI prediction for motor control in a spherical parallel kinematic structure.
  • To develop and test a method for calibrating motor control using AI to compensate for mechanical imperfections.
  • To evaluate the precision of effector positioning achieved through AI-driven calibration.

Main Methods:

  • Designed and built a prototype spherical parallel kinematic structure with three degrees of freedom, utilizing the "Agile eye" kinematic model.
  • Employed AI prediction for motor control and calibration of the mechanism, accounting for 3D-printed and lathed component clearances.
  • Conducted experiments to assess the effectiveness of AI-based calibration in achieving precise effector positioning.

Main Results:

  • AI prediction successfully controlled the motors for effector positioning in the kinematic mechanism.
  • The AI calibration method effectively compensated for inaccuracies and clearances in the mechanical components.
  • Achieved precise control of the device's optical axis, verifying the effectiveness of the AI approach.

Conclusions:

  • AI-based motor control and calibration offer an efficient solution for enhancing precision in mechatronic systems.
  • The developed method demonstrates the potential of AI to overcome limitations posed by mechanical imperfections.
  • Further mechanical design improvements could potentially reduce the need for such calibration methods.