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Area of Science:

  • Robotics
  • Control Systems
  • Machine Learning

Background:

  • Industrial robots exhibit lower absolute positioning accuracy compared to repetitive tasks.
  • Existing algorithms often suffer from low accuracy or local optima issues.

Purpose of the Study:

  • To propose an advanced error compensation algorithm for industrial robots.
  • To improve the absolute positioning accuracy of industrial robots.

Main Methods:

  • Kinematic parameter calibration using an enhanced Dog Leg algorithm.
  • Odd point error prediction with Particle Swarm Optimization Neural Network (PSONN).
  • Positioning error calculation via Spatial Grid Multipoint Interpolation (SGMI).

Main Results:

  • Kinematic calibration reduced positioning error from 3.158 mm to 0.406 mm.
  • SGMI compensation further decreased positioning error to 0.0685 mm.
  • The algorithm effectively calibrated kinematic parameters and reduced uncertainty.

Conclusions:

  • The proposed algorithm integrates traditional interpretability with neural network non-linearity.
  • It successfully addresses limitations of traditional methods and neural networks.
  • The SGMI algorithm significantly enhances industrial robot positioning accuracy.