{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T19:28:32Z","timestamp":1773862112720,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T00:00:00Z","timestamp":1736467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Guangxi science and technology major program","award":["No. AA18118002"],"award-info":[{"award-number":["No. AA18118002"]}]},{"name":"Innovation project of Guangxi Graduate Education under grant","award":["No. YCBZ2024015"],"award-info":[{"award-number":["No. YCBZ2024015"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Intel Serv Robotics"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11370-024-00581-y","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T17:30:41Z","timestamp":1736530241000},"page":"247-260","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Improving the absolute positioning accuracy of industrial robots based on OP-ELM and an enhanced backtracking search algorithm"],"prefix":"10.1007","volume":"18","author":[{"given":"Haihong","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingqi","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lulu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5927-571X","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"581_CR1","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.precisioneng.2021.11.010","volume":"74","author":"SM Cao","year":"2022","unstructured":"Cao SM, Cheng QL, Guo YJ, Zhu WD, Wang HJ, Ke YL (2022) Pose error compensation based on joint space division for 6-DOF robot manipulators. Precis Eng 74:195\u2013204. https:\/\/doi.org\/10.1016\/j.precisioneng.2021.11.010","journal-title":"Precis Eng"},{"key":"581_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107263","volume":"127","author":"DD Chen","year":"2024","unstructured":"Chen DD, Lv P, Xue L, Xing HW, Lu LX, Kong DD (2024) Positional error compensation for aviation drilling robot based on Bayesian linear regression. Eng Appl Artif Intell 127:107263. https:\/\/doi.org\/10.1016\/j.engappai.2023.107263","journal-title":"Eng Appl Artif Intell"},{"issue":"11","key":"581_CR3","doi-asserted-by":"publisher","first-page":"10831","DOI":"10.1109\/TII.2023.3241614","volume":"19","author":"WY Yang","year":"2023","unstructured":"Yang WY, Li S, Li ZB, Luo X (2023) Highly accurate manipulator calibration via extended kalman filter-incorporated residual neural network. IEEE Trans Industr Inf 19(11):10831\u201310841. https:\/\/doi.org\/10.1109\/TII.2023.3241614","journal-title":"IEEE Trans Industr Inf"},{"issue":"1","key":"581_CR4","first-page":"51","volume":"29","author":"XR Xue","year":"2023","unstructured":"Xue XR, Zhang CR, Hu TL, Chen QZ, Ding XZ (2023) Hierarchical calibration method of industrial robots based on PSO-SVR algorithm. Comput Integr Manuf Syst 29(1):51\u201360","journal-title":"Comput Integr Manuf Syst"},{"issue":"11\u201312","key":"581_CR5","doi-asserted-by":"publisher","first-page":"5431","DOI":"10.1007\/s00170-023-10957-6","volume":"125","author":"SD Ma","year":"2023","unstructured":"Ma SD, Deng KN, Lu Y, Xu X (2023) Robot error compensation based on incremental extreme learning machines and an improved sparrow search algorithm. Int J Adv Manuf Technol 125(11\u201312):5431\u20135443. https:\/\/doi.org\/10.1007\/s00170-023-10957-6","journal-title":"Int J Adv Manuf Technol"},{"issue":"12","key":"581_CR6","doi-asserted-by":"publisher","first-page":"125010","DOI":"10.1088\/1361-6501\/ab3311","volume":"30","author":"DD Chen","year":"2019","unstructured":"Chen DD, Wang TM, Yuan PJ, Sun N, Tang HY (2019) A positional error compensation method for industrial robots combining error similarity and radial basis function neural network. Meas Sci Technol 30(12):125010. https:\/\/doi.org\/10.1088\/1361-6501\/ab3311","journal-title":"Meas Sci Technol"},{"key":"581_CR7","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1016\/j.neucom.2014.03.085","volume":"151","author":"HN Nguyen","year":"2015","unstructured":"Nguyen HN, Zhou J, Kang HJ (2015) A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network. Neurocomputing 151:996\u20131005. https:\/\/doi.org\/10.1016\/j.neucom.2014.03.085","journal-title":"Neurocomputing"},{"issue":"1","key":"581_CR8","doi-asserted-by":"publisher","first-page":"168781401882293","DOI":"10.1177\/1687814018822935","volume":"11","author":"HN Nguyen","year":"2019","unstructured":"Nguyen HN, Le PN, Kang HJ (2019) A new calibration method for enhancing robot position accuracy by combining a robot model-based identification approach and an artificial neural network-based error compen-sation technique. Adv Mech Eng 11(1):1687814018822935. https:\/\/doi.org\/10.1177\/1687814018822935","journal-title":"Adv Mech Eng"},{"key":"581_CR9","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.rcim.2019.03.007","volume":"59","author":"G Zhao","year":"2019","unstructured":"Zhao G, Zhang PF, Ma GC, Xiao WL (2019) System identification of the nonlinear residual errors of an industrial robot using massive measurements. Robot Comput Integr Manuf 59:104\u2013114. https:\/\/doi.org\/10.1016\/j.rcim.2019.03.007","journal-title":"Robot Comput Integr Manuf"},{"issue":"1\u20133","key":"581_CR10","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501. https:\/\/doi.org\/10.1016\/j.neucom.2005.12.126","journal-title":"Neurocomputing"},{"issue":"5","key":"581_CR11","doi-asserted-by":"publisher","first-page":"051011","DOI":"10.1115\/1.4053824","volume":"14","author":"NY Shen","year":"2022","unstructured":"Shen NY, Yuan HM, Li J, Wang ZR, Geng L, Shi HE, Lu NH (2022) Efficient model-free calibration of a 5-degree of freedom hybrid robot. J Mech Robot Trans ASME 14(5):051011. https:\/\/doi.org\/10.1115\/1.4053824","journal-title":"J Mech Robot Trans ASME"},{"key":"581_CR12","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.apenergy.2019.04.126","volume":"249","author":"VC Mariani","year":"2019","unstructured":"Mariani VC, Och SH, Coelho LD, Domingues E (2019) Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models. Appl Energy 249:204\u2013221. https:\/\/doi.org\/10.1016\/j.apenergy.2019.04.126","journal-title":"Appl Energy"},{"key":"581_CR13","doi-asserted-by":"publisher","unstructured":"Cao WP, Gao JZ, Ming Z, Cai SB (2017) Some tricks in parameter selection for extreme learning machine. In: 2017 International conference on artificial intelligence applications and technologies (AIAAT 2017) 261:012002. https:\/\/doi.org\/10.1088\/1757-899X\/261\/1\/012002","DOI":"10.1088\/1757-899X\/261\/1\/012002"},{"issue":"11\u201312","key":"581_CR14","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.1007\/s00170-023-10856-w","volume":"125","author":"TC Gao","year":"2023","unstructured":"Gao TC, Meng F, Zhang XY, Tian ZC, Song HW (2023) An operational calibration approach of industrial robots through a motion capture system and an artificial neural network ELM. Int J Adv Manuf Technol 125(11\u201312):5135\u20135147. https:\/\/doi.org\/10.1007\/s00170-023-10856-w","journal-title":"Int J Adv Manuf Technol"},{"issue":"10","key":"581_CR15","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.patcog.2005.03.028","volume":"38","author":"QY Zhu","year":"2005","unstructured":"Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recognit 38(10):1759\u20131763. https:\/\/doi.org\/10.1016\/j.patcog.2005.03.028","journal-title":"Pattern Recognit"},{"issue":"6","key":"581_CR16","doi-asserted-by":"publisher","first-page":"1987","DOI":"10.1109\/TCSII.2020.3034771","volume":"68","author":"CX Li","year":"2021","unstructured":"Li CX, Zhu S, Sun ZB, Rogers J (2021) BAS optimized ELM for KUKA iiwa robot learning. IEEE Trans Circuits Syst II-Express Briefs 68(6):1987\u20131991. https:\/\/doi.org\/10.1109\/TCSII.2020.3034771","journal-title":"IEEE Trans Circuits Syst II-Express Briefs"},{"issue":"10","key":"581_CR17","first-page":"3000","volume":"36","author":"YM Feng","year":"2019","unstructured":"Feng YM, Dong XC, Jin T (2019) Research on methods of robot accuracy compensation based on PSO-ELM. Appl Res Comput 36(10):3000\u20133003","journal-title":"Appl Res Comput"},{"issue":"29","key":"581_CR18","doi-asserted-by":"publisher","first-page":"41611","DOI":"10.1007\/s11042-021-11007-7","volume":"81","author":"J Wang","year":"2022","unstructured":"Wang J, Lu SY, Wang SH, Zhang YD (2022) A review on extreme learning machine. Multimedia Tools Appl 81(29):41611\u201341660. https:\/\/doi.org\/10.1007\/s11042-021-11007-7","journal-title":"Multimedia Tools Appl"},{"issue":"5","key":"581_CR19","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s40430-022-03487-x","volume":"44","author":"G Chen","year":"2022","unstructured":"Chen G, Yang JZ, Xiang H, Ou DJ (2022) New positional accuracy calibration method for an autonomous robotic inspection system. J Braz Soc Mech Sci Eng 44(5):177. https:\/\/doi.org\/10.1007\/s40430-022-03487-x","journal-title":"J Braz Soc Mech Sci Eng"},{"issue":"2","key":"581_CR20","first-page":"206","volume":"41","author":"HD Dai","year":"2019","unstructured":"Dai HD, Zeng XP, You HX, Su SJ, Zeng YD, Lin ZR (2019) Pose measurement and error compensation of the robot end-effector based on an optical tracking system. Robot 41(2):206\u2013215","journal-title":"Robot"},{"issue":"3","key":"581_CR21","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1108\/IR-02-2018-0036","volume":"45","author":"Y Cai","year":"2018","unstructured":"Cai Y, Yuan PJ, Chen DD (2018) A flexible calibration method connecting the joint space and the working space of industrial robots. Ind Robot Int J Robot Res Appl 45(3):407\u2013415. https:\/\/doi.org\/10.1108\/IR-02-2018-0036","journal-title":"Ind Robot Int J Robot Res Appl"},{"key":"581_CR22","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/aacd6e","author":"DD Chen","year":"2018","unstructured":"Chen DD, Yuan PJ, Wang TM, Ying C, Tang HY (2018) A compensation method based on error similarity and error correlation to enhance the position accuracy of an aviation drilling robot. Meas Sci Technol. https:\/\/doi.org\/10.1088\/1361-6501\/aacd6e","journal-title":"Meas Sci Technol"},{"key":"581_CR23","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1016\/j.ijleo.2019.02.069","volume":"183","author":"ZX Xie","year":"2019","unstructured":"Xie ZX, Zong PF, Yao P, Ren P (2019) Calibration of 6-DOF industrial robots based on line structured light. Optik 183:1166\u20131178. https:\/\/doi.org\/10.1016\/j.ijleo.2019.02.069","journal-title":"Optik"},{"issue":"12","key":"581_CR24","doi-asserted-by":"publisher","first-page":"3565","DOI":"10.1016\/j.ijleo.2019.02.069","volume":"41","author":"L Chen","year":"2023","unstructured":"Chen L, Nie PG, Meng CQ, Chen XH, Jia BQ, Pan HH (2023) Robot 10\nparameter compensation method based on Newton-Raphson method. Robotica\n41(12):3565\u20133583. https:\/\/doi.org\/10.1017\/S026357472300108X","journal-title":"Robotica"},{"issue":"2","key":"581_CR25","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cja.2021.03.027","volume":"35","author":"B Li","year":"2022","unstructured":"Li B, Tian W, Zhang CF, Hua FF, Cui GY, Li YF (2022) Positioning error compensation of an industrial robot using neural networks and experimental study. Chin J Aeronaut 35(2):346\u2013360. https:\/\/doi.org\/10.1016\/j.cja.2021.03.027","journal-title":"Chin J Aeronaut"},{"issue":"1","key":"581_CR26","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/TNN.2009.2036259","volume":"21","author":"Y Miche","year":"2010","unstructured":"Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158\u2013162. https:\/\/doi.org\/10.1109\/TNN.2009.2036259","journal-title":"IEEE Trans Neural Netw"},{"key":"581_CR27","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/11550907_16","volume":"3697","author":"T Simil\u00e4","year":"2005","unstructured":"Simil\u00e4 T, Tikka J (2005) Multiresponse sparse regression with application to multidimensional scaling. Int Conf Artif Neural Netw 3697:97\u2013102. https:\/\/doi.org\/10.1007\/11550907_16","journal-title":"Int Conf Artif Neural Netw"},{"issue":"15","key":"581_CR28","doi-asserted-by":"publisher","first-page":"8121","DOI":"10.1016\/j.amc.2013.02.01","volume":"219","author":"P Civicioglu","year":"2013","unstructured":"Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121\u20138144. https:\/\/doi.org\/10.1016\/j.amc.2013.02.01","journal-title":"Appl Math Comput"},{"issue":"11","key":"581_CR29","doi-asserted-by":"publisher","first-page":"13187","DOI":"10.1007\/s10462-023-10470-y","volume":"56","author":"K Rajwar","year":"2023","unstructured":"Rajwar K, Deep K, Das S (2023) An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev 56(11):13187\u201313257. https:\/\/doi.org\/10.1007\/s10462-023-10470-y","journal-title":"Artif Intell Rev"},{"issue":"9","key":"581_CR30","first-page":"2543","volume":"34","author":"XJ Wang","year":"2014","unstructured":"Wang XJ, Liu SY, Tian WK (2014) Improved backtracking search optimization algorithm with new effective mutation scale factor and greedy crossover strategy. J Comput Appl 34(9):2543\u20132546","journal-title":"J Comput Appl"},{"issue":"9","key":"581_CR31","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1002\/nag.3059","volume":"44","author":"YF Jin","year":"2020","unstructured":"Jin YF, Yin ZY (2020) Enhancement of backtracking search algorithm for identifying soil parameters. Int J Numer Anal Methods Geomech 44(9):1239\u20131261. https:\/\/doi.org\/10.1002\/nag.3059","journal-title":"Int J Numer Anal Methods Geomech"}],"container-title":["Intelligent Service Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-024-00581-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11370-024-00581-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-024-00581-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T09:26:03Z","timestamp":1743845163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11370-024-00581-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,10]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["581"],"URL":"https:\/\/doi.org\/10.1007\/s11370-024-00581-y","relation":{},"ISSN":["1861-2776","1861-2784"],"issn-type":[{"value":"1861-2776","type":"print"},{"value":"1861-2784","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,10]]},"assertion":[{"value":"27 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}