Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model
This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibrat...
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| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-15 |
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| creator | Chen, Xiaoyan Sun, Yilin Zhang, Qiuju |
| description | This study addresses the problem of nonlinear error predictive compensation to achieve high positioning accuracy for advanced industrial applications. An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data). |
| doi_str_mv | 10.1155/2020/3981081 |
| format | Article |
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An improved calibration method based on the generalisation performance evaluation is proposed to enhance the stability and accuracy of robot calibration. With the development of technology, a deep neural network (DNN) optimised by a genetic algorithm (GA) is applied to predict the nonlinear error of the calibrated robot. To address the change of external payload, an extra compliance error model is established with a linear piecewise method. A global compensation method combining the GA-DNN nonlinear regression prediction model and the compliance error model is then proposed to achieve the robot’s high-precision positioning performance under any external payload. Experimental results obtained on a Staubli RX160L robot with a FARO laser tracker are introduced to demonstrate the effectiveness and benefits of our proposed methodology. The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/3981081</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Adaptation ; Artificial neural networks ; Calibration ; Compliance ; Error analysis ; Error compensation ; Genetic algorithms ; Identification ; Industrial applications ; Kinematics ; Neural networks ; Performance evaluation ; Prediction models ; Regression models ; Robots ; Stability analysis ; Statistical analysis</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-15</ispartof><rights>Copyright © 2020 Xiaoyan Chen et al.</rights><rights>Copyright © 2020 Xiaoyan Chen et al. 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The enhanced positioning accuracy can reach 0.22 mm with 98% probability (i.e., the maximum positioning error in all test data).</description><subject>Accuracy</subject><subject>Adaptation</subject><subject>Artificial neural networks</subject><subject>Calibration</subject><subject>Compliance</subject><subject>Error analysis</subject><subject>Error compensation</subject><subject>Genetic algorithms</subject><subject>Identification</subject><subject>Industrial applications</subject><subject>Kinematics</subject><subject>Neural networks</subject><subject>Performance evaluation</subject><subject>Prediction models</subject><subject>Regression models</subject><subject>Robots</subject><subject>Stability analysis</subject><subject>Statistical analysis</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkE1Lw0AQhoMoWKs3z7LgUWN39iMfxxprFWwFUfAWpsnEpqS7dZOq_femTcGjpxlmnncGHs87B34DoPVAcMEHMo6AR3Dg9UAH0tegwsO250L5IOT7sXdS1wvOBWiIet5m9GWrdVNag27DXuzMNizBqpw53A4ZmpxNralKQ-hYYpcrMnW3mlAzt7mt7MeG3WJNOWuH46F_N53uYmjY6KdxuEtVJZqM2Mg569jE5lSdekcFVjWd7Wvfe7sfvSYP_tPz-DEZPvmZEkHjy1mEHDIVxCEnCiWQDkUQxaAzjSISgSKtEQoluSjiQiAJGbc24oAKDYiy7112d1fOfq6pbtKFXTvTvkyFEqEQwEPVUtcdlTlb146KdOXKZeskBZ5u5aZbuelebotfdfi8NDl-l__RFx1NLUMF_tEQq0hp-Qs4VoIv</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Chen, Xiaoyan</creator><creator>Sun, Yilin</creator><creator>Zhang, Qiuju</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-7002-5444</orcidid><orcidid>https://orcid.org/0000-0003-0773-7120</orcidid><orcidid>https://orcid.org/0000-0002-0885-2588</orcidid></search><sort><creationdate>2020</creationdate><title>Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model</title><author>Chen, Xiaoyan ; 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| subjects | Accuracy Adaptation Artificial neural networks Calibration Compliance Error analysis Error compensation Genetic algorithms Identification Industrial applications Kinematics Neural networks Performance evaluation Prediction models Regression models Robots Stability analysis Statistical analysis |
| title | Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model |
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