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

Gespeichert in:

Autor*in:	Kejin Liu [verfasserIn] Junyong Xia [verfasserIn] Fei Zhong [verfasserIn] Li Zhang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: International Journal of Advanced Robotic Systems - SAGE Publishing, 2008, 19(2022)
Übergeordnetes Werk:	volume:19 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1177/17298806221082398

Katalog-ID:	DOAJ016009134

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ016009134
003	DE-627
005	20230310080427.0
007	cr uuu---uuuuu
008	230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1177/17298806221082398 \|2 doi
035			\|a (DE-627)DOAJ016009134
035			\|a (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK7800-8360
050		0	\|a QA75.5-76.95
100	0		\|a Kejin Liu \|e verfasserin \|4 aut
245	1	0	\|a Structural parameters identification for industrial robot using a hybrid algorithm
264		1	\|c 2022
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a 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.
653		0	\|a Electronics
653		0	\|a Electronic computers. Computer science
700	0		\|a Junyong Xia \|e verfasserin \|4 aut
700	0		\|a Fei Zhong \|e verfasserin \|4 aut
700	0		\|a Li Zhang \|e verfasserin \|4 aut
773	0	8	\|i In \|t International Journal of Advanced Robotic Systems \|d SAGE Publishing, 2008 \|g 19(2022) \|w (DE-627)500017794 \|w (DE-600)2202393-8 \|x 17298814 \|7 nnns
773	1	8	\|g volume:19 \|g year:2022
856	4	0	\|u https://doi.org/10.1177/17298806221082398 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 \|z kostenfrei
856	4	0	\|u https://doi.org/10.1177/17298806221082398 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1729-8814 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_374
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2706
912			\|a GBV_ILN_2707
912			\|a GBV_ILN_2890
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 19 \|j 2022

Indexfelder

author_variant	k l kl j x jx f z fz l z lz
matchkey_str	article:17298814:2022----::tutrlaaeesdniiainoidsrarbts
hierarchy_sort_str	2022
callnumber-subject-code	TK
publishDate	2022
allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	In International Journal of Advanced Robotic Systems 19(2022) volume:19 year:2022
sourceStr	In International Journal of Advanced Robotic Systems 19(2022) volume:19 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Electronics Electronic computers. Computer science
isfreeaccess_bool	true
container_title	International Journal of Advanced Robotic Systems
authorswithroles_txt_mv	Kejin Liu @@aut@@ Junyong Xia @@aut@@ Fei Zhong @@aut@@ Li Zhang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	500017794
id	DOAJ016009134
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ016009134</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310080427.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1177/17298806221082398</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ016009134</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ1449d271be83438ab46781cf0006b2d9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK7800-8360</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Kejin Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Structural parameters identification for industrial robot using a hybrid algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junyong Xia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fei Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Li Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Journal of Advanced Robotic Systems</subfield><subfield code="d">SAGE Publishing, 2008</subfield><subfield code="g">19(2022)</subfield><subfield code="w">(DE-627)500017794</subfield><subfield code="w">(DE-600)2202393-8</subfield><subfield code="x">17298814</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2022</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1177/17298806221082398</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/1449d271be83438ab46781cf0006b2d9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1177/17298806221082398</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1729-8814</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2706</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2707</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2890</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2022</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Kejin Liu
spellingShingle	Kejin Liu misc TK7800-8360 misc QA75.5-76.95 misc Electronics misc Electronic computers. Computer science Structural parameters identification for industrial robot using a hybrid algorithm
authorStr	Kejin Liu
ppnlink_with_tag_str_mv	@@773@@(DE-627)500017794
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK7800-8360
illustrated	Not Illustrated
issn	17298814
topic_title	TK7800-8360 QA75.5-76.95 Structural parameters identification for industrial robot using a hybrid algorithm
topic	misc TK7800-8360 misc QA75.5-76.95 misc Electronics misc Electronic computers. Computer science
topic_unstemmed	misc TK7800-8360 misc QA75.5-76.95 misc Electronics misc Electronic computers. Computer science
topic_browse	misc TK7800-8360 misc QA75.5-76.95 misc Electronics misc Electronic computers. Computer science
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International Journal of Advanced Robotic Systems
hierarchy_parent_id	500017794
hierarchy_top_title	International Journal of Advanced Robotic Systems
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)500017794 (DE-600)2202393-8
title	Structural parameters identification for industrial robot using a hybrid algorithm
ctrlnum	(DE-627)DOAJ016009134 (DE-599)DOAJ1449d271be83438ab46781cf0006b2d9
title_full	Structural parameters identification for industrial robot using a hybrid algorithm
author_sort	Kejin Liu
journal	International Journal of Advanced Robotic Systems
journalStr	International Journal of Advanced Robotic Systems
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2022
contenttype_str_mv	txt
author_browse	Kejin Liu Junyong Xia Fei Zhong Li Zhang
container_volume	19
class	TK7800-8360 QA75.5-76.95
format_se	Elektronische Aufsätze
author-letter	Kejin Liu
doi_str_mv	10.1177/17298806221082398
author2-role	verfasserin
title_sort	structural parameters identification for industrial robot using a hybrid algorithm
callnumber	TK7800-8360
title_auth	Structural parameters identification for industrial robot using a hybrid algorithm
abstract	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.
collection_details	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
title_short	Structural parameters identification for industrial robot using a hybrid algorithm
url	https://doi.org/10.1177/17298806221082398 https://doaj.org/article/1449d271be83438ab46781cf0006b2d9 https://doaj.org/toc/1729-8814
remote_bool	true
author2	Junyong Xia Fei Zhong Li Zhang
author2Str	Junyong Xia Fei Zhong Li Zhang
ppnlink	500017794
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1177/17298806221082398
callnumber-a	TK7800-8360
up_date	2024-07-03T18:27:04.297Z
_version_	1803583461051596800
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ016009134</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230310080427.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1177/17298806221082398</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ016009134</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ1449d271be83438ab46781cf0006b2d9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK7800-8360</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA75.5-76.95</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Kejin Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Structural parameters identification for industrial robot using a hybrid algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic computers. Computer science</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junyong Xia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Fei Zhong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Li Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International Journal of Advanced Robotic Systems</subfield><subfield code="d">SAGE Publishing, 2008</subfield><subfield code="g">19(2022)</subfield><subfield code="w">(DE-627)500017794</subfield><subfield code="w">(DE-600)2202393-8</subfield><subfield code="x">17298814</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:19</subfield><subfield code="g">year:2022</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1177/17298806221082398</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/1449d271be83438ab46781cf0006b2d9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1177/17298806221082398</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1729-8814</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_374</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2706</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2707</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2890</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">19</subfield><subfield code="j">2022</subfield></datafield></record></collection>
score	7.400567

Nicht das Richtige dabei?

Schreiben Sie uns!

Structural parameters identification for industrial robot using a hybrid algorithm

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?