Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots
Large-scale high-end equipment robotic precision operations have large workspaces and numerous uncertainties. They have an unevenly distributed error effect on the position in space. The current conventional stereotyping method ignores the uncertainty in the robotic system; the probabilistic or fuzz...
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/35 |
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| author | Jianping Sun Weian Yang Xin Meng Jun Peng Zhaoping Tang |
| author_facet | Jianping Sun Weian Yang Xin Meng Jun Peng Zhaoping Tang |
| author_sort | Jianping Sun |
| collection | DOAJ |
| description | Large-scale high-end equipment robotic precision operations have large workspaces and numerous uncertainties. They have an unevenly distributed error effect on the position in space. The current conventional stereotyping method ignores the uncertainty in the robotic system; the probabilistic or fuzzy method is often due to the lack of statistical samples in the project, and it is difficult to accurately define the probabilistic or fuzzy model because the probabilistic distribution pattern or fuzzy affiliation cannot be known in advance. In this paper, we propose a non-probabilistic reliability-based robot workspace partitioning method that only needs to know the upper and lower bounds of the values of the uncertain parameters and is adapted to realize accurate calibration of robots in small-sample or information-poor scenarios. The method considers the differences in non-probabilistic reliability of robot end positions in different workspace ranges and uses KLFCM clustering combined with a genetic algorithm to perform a two-stage hierarchical partitioning optimization. The experimental results show that compared with the global compensation, the average values of the upper and lower limits of the x, y, and z direction error intervals of the partitioned compensation are reduced by 31.17%, 7.26%, 14.30%, 34.91%, 2.48%, and 35.82%, respectively, verifying that the method in this study can more accurately realize the partitioned categorization calibration and compensation of the robot, and effectively improve the reliability and spatial adaptability of parameter calibration and compensation of the robot in the full workspace domain. The reliability and spatial adaptability of the parameter calibration and compensation are effectively improved in the entire workspace domain of the robot. |
| format | Article |
| id | doaj-art-36b4690ecafb4bd29112d9d443737103 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-36b4690ecafb4bd29112d9d4437371032025-01-24T13:39:13ZengMDPI AGMachines2075-17022025-01-011313510.3390/machines13010035Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace RobotsJianping Sun0Weian Yang1Xin Meng2Jun Peng3Zhaoping Tang4School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaLarge-scale high-end equipment robotic precision operations have large workspaces and numerous uncertainties. They have an unevenly distributed error effect on the position in space. The current conventional stereotyping method ignores the uncertainty in the robotic system; the probabilistic or fuzzy method is often due to the lack of statistical samples in the project, and it is difficult to accurately define the probabilistic or fuzzy model because the probabilistic distribution pattern or fuzzy affiliation cannot be known in advance. In this paper, we propose a non-probabilistic reliability-based robot workspace partitioning method that only needs to know the upper and lower bounds of the values of the uncertain parameters and is adapted to realize accurate calibration of robots in small-sample or information-poor scenarios. The method considers the differences in non-probabilistic reliability of robot end positions in different workspace ranges and uses KLFCM clustering combined with a genetic algorithm to perform a two-stage hierarchical partitioning optimization. The experimental results show that compared with the global compensation, the average values of the upper and lower limits of the x, y, and z direction error intervals of the partitioned compensation are reduced by 31.17%, 7.26%, 14.30%, 34.91%, 2.48%, and 35.82%, respectively, verifying that the method in this study can more accurately realize the partitioned categorization calibration and compensation of the robot, and effectively improve the reliability and spatial adaptability of parameter calibration and compensation of the robot in the full workspace domain. The reliability and spatial adaptability of the parameter calibration and compensation are effectively improved in the entire workspace domain of the robot.https://www.mdpi.com/2075-1702/13/1/35industrial robotspace partitionjoint clearancenon-probabilistic reliabilityKLFCM algorithm |
| spellingShingle | Jianping Sun Weian Yang Xin Meng Jun Peng Zhaoping Tang Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots Machines industrial robot space partition joint clearance non-probabilistic reliability KLFCM algorithm |
| title | Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots |
| title_full | Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots |
| title_fullStr | Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots |
| title_full_unstemmed | Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots |
| title_short | Research on Non-Probabilistic Reliability Partitioning Methods for Large Workspace Robots |
| title_sort | research on non probabilistic reliability partitioning methods for large workspace robots |
| topic | industrial robot space partition joint clearance non-probabilistic reliability KLFCM algorithm |
| url | https://www.mdpi.com/2075-1702/13/1/35 |
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