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Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network.

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  • 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy.

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Summary

This study introduces a model-free technique using multi-layer neural networks (MNN) for accurate robot tool dynamic identification. This approach improves control precision in physical interaction tasks like robot-assisted surgery.

Keywords:
calibrationmodel-freemulti-layer neural networktool dynamic identification

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Area of Science:

  • Robotics
  • Control Systems
  • Machine Learning

Background:

  • Accurate interaction force sensing is crucial for precise robot control in tasks like surgery and teleoperation.
  • Existing methods often struggle with tool dynamics, as force sensors measure both interaction and gravity forces, necessitating tool dynamic identification for model-based control.

Purpose of the Study:

  • To propose and evaluate a model-free technique for dynamic identification of robot tool dynamics.
  • To enhance the accuracy of dynamic models compared to traditional model-based methods.

Main Methods:

  • Utilized multi-layer neural networks (MNN), specifically feed-forward (FF-MNN) and cascade-forward (CF-MNN) architectures, for model-free dynamic identification.
  • Compared the MNN approach against a model-based curve fitting (CF) technique.

Main Results:

  • The MNN-based model-free technique demonstrated improved accuracy in tool dynamic identification compared to the model-based curve fitting method.
  • Demonstrated successful application in bilateral teleoperation using a KUKA LWR4+ robot and a Force Dimension SIGMA 7 manipulator.

Conclusions:

  • The proposed model-free dynamic identification using MNN offers a promising advancement over model-based methods.
  • This technique enhances control precision and performance in robot-assisted surgery and bilateral teleoperation systems.