Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network
Articulated arm coordinate measuring machine (AACMM) is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the acc...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/5156264 |
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| author | Guanbin Gao Hongwei Zhang Hongjun San Xing Wu Wen Wang |
| author_facet | Guanbin Gao Hongwei Zhang Hongjun San Xing Wu Wen Wang |
| author_sort | Guanbin Gao |
| collection | DOAJ |
| description | Articulated arm coordinate measuring machine (AACMM) is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM. |
| format | Article |
| id | doaj-art-78e1bf9c9f964d15ba05c035d51a0fb8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-78e1bf9c9f964d15ba05c035d51a0fb82025-02-03T01:28:56ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/51562645156264Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural NetworkGuanbin Gao0Hongwei Zhang1Hongjun San2Xing Wu3Wen Wang4Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaArticulated arm coordinate measuring machine (AACMM) is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM.http://dx.doi.org/10.1155/2017/5156264 |
| spellingShingle | Guanbin Gao Hongwei Zhang Hongjun San Xing Wu Wen Wang Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network Complexity |
| title | Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network |
| title_full | Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network |
| title_fullStr | Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network |
| title_full_unstemmed | Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network |
| title_short | Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network |
| title_sort | modeling and error compensation of robotic articulated arm coordinate measuring machines using bp neural network |
| url | http://dx.doi.org/10.1155/2017/5156264 |
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