Structural parameters identification for industrial robot using a hybrid algorithm
To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of rele...
Ausführliche Beschreibung
Autor*in: |
Kejin Liu [verfasserIn] Junyong Xia [verfasserIn] Fei Zhong [verfasserIn] Li Zhang [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Übergeordnetes Werk: |
In: International Journal of Advanced Robotic Systems - SAGE Publishing, 2008, 19(2022) |
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Übergeordnetes Werk: |
volume:19 ; year:2022 |
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DOI / URN: |
10.1177/17298806221082398 |
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Katalog-ID: |
DOAJ016009134 |
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10.1177/17298806221082398 doi (DE-627)DOAJ016009134 (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Kejin Liu verfasserin aut Structural parameters identification for industrial robot using a hybrid algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. Electronics Electronic computers. Computer science Junyong Xia verfasserin aut Fei Zhong verfasserin aut Li Zhang verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 19(2022) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:19 year:2022 https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 kostenfrei https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/toc/1729-8814 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2022 |
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10.1177/17298806221082398 doi (DE-627)DOAJ016009134 (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Kejin Liu verfasserin aut Structural parameters identification for industrial robot using a hybrid algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. Electronics Electronic computers. Computer science Junyong Xia verfasserin aut Fei Zhong verfasserin aut Li Zhang verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 19(2022) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:19 year:2022 https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 kostenfrei https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/toc/1729-8814 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2022 |
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10.1177/17298806221082398 doi (DE-627)DOAJ016009134 (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Kejin Liu verfasserin aut Structural parameters identification for industrial robot using a hybrid algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. Electronics Electronic computers. Computer science Junyong Xia verfasserin aut Fei Zhong verfasserin aut Li Zhang verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 19(2022) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:19 year:2022 https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 kostenfrei https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/toc/1729-8814 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2022 |
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10.1177/17298806221082398 doi (DE-627)DOAJ016009134 (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Kejin Liu verfasserin aut Structural parameters identification for industrial robot using a hybrid algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. Electronics Electronic computers. Computer science Junyong Xia verfasserin aut Fei Zhong verfasserin aut Li Zhang verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 19(2022) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:19 year:2022 https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 kostenfrei https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/toc/1729-8814 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2022 |
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10.1177/17298806221082398 doi (DE-627)DOAJ016009134 (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9 DE-627 ger DE-627 rakwb eng TK7800-8360 QA75.5-76.95 Kejin Liu verfasserin aut Structural parameters identification for industrial robot using a hybrid algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. Electronics Electronic computers. Computer science Junyong Xia verfasserin aut Fei Zhong verfasserin aut Li Zhang verfasserin aut In International Journal of Advanced Robotic Systems SAGE Publishing, 2008 19(2022) (DE-627)500017794 (DE-600)2202393-8 17298814 nnns volume:19 year:2022 https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 kostenfrei https://doi.org/10.1177/17298806221082398 kostenfrei https://doaj.org/toc/1729-8814 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2706 GBV_ILN_2707 GBV_ILN_2890 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2022 |
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To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. |
abstractGer |
To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. |
abstract_unstemmed |
To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot. |
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Structural parameters identification for industrial robot using a hybrid algorithm |
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|
score |
7.400567 |