A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space
With the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| author | Fantong Meng Jinhua Wei Qianyi Feng Zhigang Dong Renke Kang Dongming Guo Jiankun Yang |
| author_facet | Fantong Meng Jinhua Wei Qianyi Feng Zhigang Dong Renke Kang Dongming Guo Jiankun Yang |
| author_sort | Fantong Meng |
| collection | DOAJ |
| description | With the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution patterns of robot errors and leverage spatial similarity for error prediction and compensation. However, existing methods in Cartesian space struggle to achieve accurate error estimation when the robot is loaded or the end-effector orientations are varied. To address these challenges, a novel method for robot error prediction and accuracy compensation within configuration space is proposed. The analysis of robot error distribution reveals that the spatial similarity of robot errors is more pronounced and stable in configuration space compared to Cartesian space, and this property exhibits significant anisotropy across joint dimensions. A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. Based on the robot error prediction model, a cyclic searching method is employed to directly compensate for the joint angles. An experimental system is established using an industrial robot equipped with a 120 kg end-effector and a laser tracker. Eighty sampling points with diverse poses are randomly selected within the task workspace to measure the robot errors before and after compensation. The proposed method achieves an error prediction accuracy of 0.172 mm, reducing the robot error from the original 4.96 mm to 0.28 mm, thus meeting the stringent accuracy requirements for hole machining in robotic aerospace assembly processes. |
| format | Article |
| id | doaj-art-2acd0e6e12f94a87aaec59b0e3c9048e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-2acd0e6e12f94a87aaec59b0e3c9048e2025-08-20T02:01:03ZengMDPI AGApplied Sciences2076-34172024-12-0114241168210.3390/app142411682A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration SpaceFantong Meng0Jinhua Wei1Qianyi Feng2Zhigang Dong3Renke Kang4Dongming Guo5Jiankun Yang6State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024, ChinaAerospace Research Institute of Material & Processing Technology, Beijing 100076, ChinaState Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian 116024, ChinaPengcheng Laboratory, Shenzhen 518055, ChinaWith the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution patterns of robot errors and leverage spatial similarity for error prediction and compensation. However, existing methods in Cartesian space struggle to achieve accurate error estimation when the robot is loaded or the end-effector orientations are varied. To address these challenges, a novel method for robot error prediction and accuracy compensation within configuration space is proposed. The analysis of robot error distribution reveals that the spatial similarity of robot errors is more pronounced and stable in configuration space compared to Cartesian space, and this property exhibits significant anisotropy across joint dimensions. A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. Based on the robot error prediction model, a cyclic searching method is employed to directly compensate for the joint angles. An experimental system is established using an industrial robot equipped with a 120 kg end-effector and a laser tracker. Eighty sampling points with diverse poses are randomly selected within the task workspace to measure the robot errors before and after compensation. The proposed method achieves an error prediction accuracy of 0.172 mm, reducing the robot error from the original 4.96 mm to 0.28 mm, thus meeting the stringent accuracy requirements for hole machining in robotic aerospace assembly processes.https://www.mdpi.com/2076-3417/14/24/11682robotic positioning compensationrobot error predictionconfiguration spacespatial similarityjoint weights optimizationrobot calibration |
| spellingShingle | Fantong Meng Jinhua Wei Qianyi Feng Zhigang Dong Renke Kang Dongming Guo Jiankun Yang A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space Applied Sciences robotic positioning compensation robot error prediction configuration space spatial similarity joint weights optimization robot calibration |
| title | A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space |
| title_full | A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space |
| title_fullStr | A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space |
| title_full_unstemmed | A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space |
| title_short | A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space |
| title_sort | robot error prediction and compensation method using joint weights optimization within configuration space |
| topic | robotic positioning compensation robot error prediction configuration space spatial similarity joint weights optimization robot calibration |
| url | https://www.mdpi.com/2076-3417/14/24/11682 |
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