A new method for recognizing geometric parameters of industrial robots
Abstract Intelligent algorithms that are commonly used to obtain errors in the geometric parameters of industrial robots have a low accuracy, easily fall into the local optimal solution, and involve complicated coding such that they are unsuitable for use in engineering. In this study, we first appl...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86971-3 |
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| author | Bin Kou Yi Zhang |
| author_facet | Bin Kou Yi Zhang |
| author_sort | Bin Kou |
| collection | DOAJ |
| description | Abstract Intelligent algorithms that are commonly used to obtain errors in the geometric parameters of industrial robots have a low accuracy, easily fall into the local optimal solution, and involve complicated coding such that they are unsuitable for use in engineering. In this study, we first apply the D-H method to establish a model of error in industrial robots, and then use the set of errors in their geometric parameters as the objective function. Following this, we improve the accuracy of global optimization of the particle swarm optimization (PSO) algorithm by drawing on the wandering behavior of the wolf pack algorithm and hybridization behavior of the genetic algorithm. We balance the convergence of the PSO algorithm by using a linearly diminishing weight. This leads to an improved PSO algorithm that can accurately determine errors in the geometric parameters of industrial robots. We compared our improve PSO algorithm with commonly used particle swarm algorithms, and the results showed that the former had a higher accuracy of convergence on average. Moreover, the errors in the geometric parameters obtained by the improved PSO algorithm can enhance the accuracy of localization of errors in industrial robots. |
| format | Article |
| id | doaj-art-e9c4c59856774adcabcc4f6178e2299f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e9c4c59856774adcabcc4f6178e2299f2025-01-26T12:33:39ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-86971-3A new method for recognizing geometric parameters of industrial robotsBin Kou0Yi Zhang1School of Software, Taiyuan University of TechnologyXi’an BZT Electronic Technology Co.Abstract Intelligent algorithms that are commonly used to obtain errors in the geometric parameters of industrial robots have a low accuracy, easily fall into the local optimal solution, and involve complicated coding such that they are unsuitable for use in engineering. In this study, we first apply the D-H method to establish a model of error in industrial robots, and then use the set of errors in their geometric parameters as the objective function. Following this, we improve the accuracy of global optimization of the particle swarm optimization (PSO) algorithm by drawing on the wandering behavior of the wolf pack algorithm and hybridization behavior of the genetic algorithm. We balance the convergence of the PSO algorithm by using a linearly diminishing weight. This leads to an improved PSO algorithm that can accurately determine errors in the geometric parameters of industrial robots. We compared our improve PSO algorithm with commonly used particle swarm algorithms, and the results showed that the former had a higher accuracy of convergence on average. Moreover, the errors in the geometric parameters obtained by the improved PSO algorithm can enhance the accuracy of localization of errors in industrial robots.https://doi.org/10.1038/s41598-025-86971-3Computer applicationsParticle swarm algorithmsIndustrial robotsLocalization errors |
| spellingShingle | Bin Kou Yi Zhang A new method for recognizing geometric parameters of industrial robots Scientific Reports Computer applications Particle swarm algorithms Industrial robots Localization errors |
| title | A new method for recognizing geometric parameters of industrial robots |
| title_full | A new method for recognizing geometric parameters of industrial robots |
| title_fullStr | A new method for recognizing geometric parameters of industrial robots |
| title_full_unstemmed | A new method for recognizing geometric parameters of industrial robots |
| title_short | A new method for recognizing geometric parameters of industrial robots |
| title_sort | new method for recognizing geometric parameters of industrial robots |
| topic | Computer applications Particle swarm algorithms Industrial robots Localization errors |
| url | https://doi.org/10.1038/s41598-025-86971-3 |
| work_keys_str_mv | AT binkou anewmethodforrecognizinggeometricparametersofindustrialrobots AT yizhang anewmethodforrecognizinggeometricparametersofindustrialrobots AT binkou newmethodforrecognizinggeometricparametersofindustrialrobots AT yizhang newmethodforrecognizinggeometricparametersofindustrialrobots |