{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:20:18Z","timestamp":1769941218698,"version":"3.49.0"},"reference-count":31,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2024,2,23]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models\u2019 outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594\u2009mm and 0.255\u2009mm, respectively.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-11-2023-0267","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T06:53:59Z","timestamp":1705992839000},"page":"314-325","source":"Crossref","is-referenced-by-count":5,"title":["A method for predicting relative position errors in dual-robot systems via knowledge transfer from geometric and nongeometric calibration"],"prefix":"10.1108","volume":"51","author":[{"given":"Siming","family":"Cao","sequence":"first","affiliation":[]},{"given":"Hongfeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yingjie","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Weidong","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yinglin","family":"Ke","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"key2024022210030387600_ref001","doi-asserted-by":"publisher","first-page":"553","DOI":"10.5220\/0009905605530559","article-title":"Deep learning with transfer learning method for error compensation of cable-driven robot","volume-title":"Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics","year":"2020"},{"issue":"8","key":"key2024022210030387600_ref002","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.mechmachtheory.2004.12.012","article-title":"A systematic technique to estimate positioning errors for robot accuracy improvement using laser interferometry based sensing","volume":"40","year":"2005","journal-title":"Mechanism and Machine Theory"},{"issue":"11\/12","key":"key2024022210030387600_ref003","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s00170-005-0278-4","article-title":"On the comparison of model-based and modeless robotic calibration based on a fuzzy interpolation method","volume":"31","year":"2007","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"key2024022210030387600_ref004","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.precisioneng.2021.11.010","article-title":"Pose error compensation based on joint space division for 6-DOF robot manipulators","volume":"74","year":"2022","journal-title":"Precision Engineering"},{"key":"key2024022210030387600_ref005","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.cirpj.2023.06.009","article-title":"Online path correction under end point nonholonomic constraints \u2013 implementation with visual sensing in robotic fiber placement","volume":"45","year":"2023","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"issue":"5","key":"key2024022210030387600_ref006","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1109\/TRO.2014.2319560","article-title":"Determination of the identifiable parameters in robot calibration based on the POE formula","volume":"30","year":"2014","journal-title":"IEEE Transactions on Robotics"},{"key":"key2024022210030387600_ref007","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.rcim.2018.07.002","article-title":"Non-kinematic calibration of industrial robots using a rigid\u2013flexible coupling error model and a full pose measurement method","volume":"57","year":"2019","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"11\/12","key":"key2024022210030387600_ref008","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1016\/S0094-114X(01)00048-9","article-title":"Local POE model for robot kinematic calibration","volume":"36","year":"2001","journal-title":"Mechanism and Machine Theory"},{"issue":"2","key":"key2024022210030387600_ref009","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1115\/1.4011045","article-title":"A kinematic notation for Lower-Pair mechanisms based on matrices","volume":"22","year":"1955","journal-title":"Journal of Applied Mechanics"},{"issue":"14","key":"key2024022210030387600_ref010","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1016\/S0890-6955(00)00023-7","article-title":"Nongeometric error identification and compensation for robotic system by inverse calibration","volume":"40","year":"2000","journal-title":"International Journal of Machine Tools and Manufacture"},{"key":"key2024022210030387600_ref011","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1109\/CDC.1983.269783","article-title":"Robot arm geometric link parameter estimation","volume-title":"The 22nd IEEE Conference on Decision and Control. 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