A new method for recognizing geometric parameters of industrial robots.

Area of Science:

• Robotics
• Computational Intelligence
• Mechanical Engineering

Background:

• Traditional intelligent algorithms for industrial robot geometric parameter error analysis suffer from low accuracy and local optima.
• Existing methods are often too complex for practical engineering applications.
• Accurate geometric error modeling is crucial for enhancing industrial robot performance.

Purpose of the Study:

• To develop a more accurate and efficient algorithm for determining geometric parameter errors in industrial robots.
• To improve the global optimization accuracy and convergence of the Particle Swarm Optimization (PSO) algorithm.
• To enhance the precision of industrial robot localization through improved error identification.

Main Methods:

• Established an industrial robot error model using the Denavit-Hartenberg (D-H) method.
• Utilized the set of geometric parameter errors as the objective function for optimization.
• Improved the Particle Swarm Optimization (PSO) algorithm by incorporating elements from the wolf pack and genetic algorithms, and using a linearly diminishing weight to balance convergence.

Main Results:

• The improved PSO algorithm demonstrated higher average accuracy in convergence compared to standard PSO algorithms.
• The algorithm successfully and accurately determined errors in the geometric parameters of industrial robots.
• The identified geometric parameter errors significantly enhanced the accuracy of industrial robot localization.

Conclusions:

• The hybrid PSO algorithm offers a superior approach for accurate geometric error modeling in industrial robots.
• This method overcomes the limitations of traditional algorithms, providing a practical engineering solution.
• Improved geometric error identification directly translates to enhanced industrial robot positioning accuracy.