A Hybrid Analytical and Data-driven Modeling Approach for Calibration of Heavy-duty Cartesian Robot
文献类型：会议论文
| 作者 | Wan, Hongyu; Chen, Silu; Liu, Yisha; Jin, Chaochao; Chen, Furu; Wang, Jin; Zhang, Chi; Yang, Guilin |
| 出版日期 | 2020 |
| 会议日期 | JUL 06-09, 2020 |
| 英文摘要 | Robot calibration is to enhance absolute positioning accuracy within robotic task space. Traditional method is to build the geometric error model and identify the deviation of kinematic parameters. In this paper, a hybrid analytical and data-driven non-geometric error modeling approach is proposed after the geometric error is compensated. It is intended to satisfy higher accuracy requirements when the heavy-duty Cartesian robot performs metal finishing tasks under different load conditions. Analytical and deterministic Beam Deformation model is built firstly to remove a large portion of the non-geometric error. By this means, data-driven Gaussian Process Regression model can achieve better prediction results due to the collected residual error data is closer to Gaussian distribution. The experimental result of platform tests demonstrates the effectiveness and robustness of the proposed method. |
| 会议录出版者 | IEEE ASME International Conference on Advanced Intelligent Mechatronics |
| 学科主题 | Engineering ; Robotics |
| ISSN号 | 2159-6255 |
| ISBN号 | 978-1-7281-6794-7 |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/23281]  |
| 专题 | 会议专题 会议专题_会议论文 |
| 推荐引用方式 GB/T 7714 | Wan, Hongyu,Chen, Silu,Liu, Yisha,et al. A Hybrid Analytical and Data-driven Modeling Approach for Calibration of Heavy-duty Cartesian Robot[C]. 见:. JUL 06-09, 2020. |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
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