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Robot Calibration Sampling Data Optimization Method Based on Improved Robot Observability Metrics and Binary

Huakun Jia1, Hanbo Zeng1, Jiyan Zhang1

  • 1College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China.

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|October 16, 2024
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Summary
This summary is machine-generated.

This study introduces a new method for optimizing robot calibration sampling points. The approach enhances end-effector positioning accuracy, reducing errors by over 28% through improved robot observability metrics and a Binary Simulated Annealing Algorithm.

Keywords:
Product of Exponentials modelcalibrationindustrial robotobservability metricssampling data optimization

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Area of Science:

  • Robotics
  • Mechanical Engineering
  • Metrology

Background:

  • Increasing demand for precision in industrial robot operations.
  • Calibration is crucial for enhancing end-effector positioning accuracy.
  • Sampling data optimization significantly impacts calibration effectiveness.

Purpose of the Study:

  • To propose a robot calibration sampling point optimization method.
  • To improve end-effector positioning accuracy using enhanced observability metrics and a Binary Simulated Annealing Algorithm (BSAA).
  • To reduce calibration errors in industrial robots.

Main Methods:

  • Established a robot kinematic model using the Product of Exponentials (POE) and a generalized error model.
  • Derived an error calibration model based on spatial single points using the least squares method.
  • Optimized calibration sampling data selection via an improved robot observability metric and BSAA, followed by nonlinear least squares for parameter error determination.

Main Results:

  • The proposed method optimized sampling points for robot calibration.
  • Reduced the average end-effector positioning error from 0.356 mm to 0.254 mm.
  • Demonstrated significant improvement in robot positioning accuracy post-calibration.

Conclusions:

  • The developed method effectively enhances robot calibration accuracy.
  • Optimized sampling strategies are vital for precise industrial robot operations.
  • The integration of improved observability metrics and BSAA offers a robust solution for robot calibration.