中文版 | English
CSpace
Highly Accurate Manipulator Calibration via Extended Kalman Filter-Incorporated Residual Neural Network
Yang, Weiyi1,2; Li, Shuai3,4; Li, Zhibin1,2; Luo, Xin5
2023-11-01
摘要With the rapid development and wide applications of industrial manipulators, a vital concern rises regarding a manipulator's absolute positioning accuracy. The manipulator calibration models have proven to be highly efficient in improving the absolute positioning accuracy of an industrial manipulator. However, existing calibration models commonly suffer from the low calibration accuracy caused by the ignorance of nongeometric errors. To address this critical issue, this article proposes an extended Kalman filter-incorporated Residual Neural Network-based Calibration (ERC) model for kinematic calibration. Its main ideas are two-fold: 1) adopting an extended Kalman filter (EKF) to address a manipulator's geometric errors; and 2) adopting a residual neural network to cascade with the EKF for eliminating the remaining nongeometric errors. Detailed experiments on three real datasets collected from industrial manipulators demonstrate that the proposed ERC model has achieved significant calibration accuracy gain over several state-of-the-art models.
关键词Data driven draw-wire sensor extended Kalman filter geometric error industrial manipulator manipulator calibration nongeometric error residual neural network (ResNN)
DOI10.1109/TII.2023.3241614
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
卷号19期号:11页码:10831-10841
通讯作者Luo, Xin(luoxin@swu.edu.cn)
收录类别SCI
WOS记录号WOS:001181996300020
语种en