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Method for robot kinematic parameters identification based on position and orientation data obtained with laser

Jindong Wang1, Chenhao Yang1, Zhanyang Wu1

  • 1School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.

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|October 7, 2024
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Summary

This study presents a robot kinematic parameter identification model using laser trackers for improved accuracy compensation. It optimizes laser tracker layout and employs a hybrid genetic algorithm for precise calibration.

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Area of Science:

  • Robotics
  • Metrology
  • Mechanical Engineering

Background:

  • Accurate robot kinematic parameter identification is crucial for effective robot accuracy compensation.
  • Existing methods may lack precision or efficiency in identifying kinematic parameter errors.

Purpose of the Study:

  • To develop a rapid and accurate method for identifying robot kinematic parameter errors.
  • To enhance robot accuracy compensation through improved kinematic parameter identification.

Main Methods:

  • Utilized laser trackers for robot pose measurement and kinematic parameter identification.
  • Analyzed laser tracker layout influence and developed an evaluation function for optimal positioning.
  • Constructed a kinematic parameter identification model integrating position and orientation data, enhanced by a hybrid genetic algorithm with simplex method for optimal calibration pose selection.

Main Results:

  • Established a mapping relationship between robot end-effector deviations and kinematic parameter errors.
  • Demonstrated that the integrated position and orientation model yields higher accuracy than position-only models.
  • Experimental results validated the effectiveness of the proposed model and optimization strategy for robot kinematic parameter identification.

Conclusions:

  • The developed kinematic parameter identification model, leveraging laser tracker measurements and an optimized calibration strategy, significantly improves robot accuracy.
  • The hybrid genetic algorithm effectively addresses premature convergence issues, leading to a more robust identification process.
  • This approach provides a reliable solution for precise robot calibration and enhanced performance.