Name
Abstract | ||
|---|---|---|
With the development of Industry 4.0, future robots will no longer be fixed on the production line for doing repetitive tasks but will be adaptable and versatile. This means that they can correct their own parameters almost instantaneously. However, traditional offline calibration methods cannot meet such requirements. This article proposes an online robot calibration method that can provide quickly identify robotic kinematic parameters without stopping the robot, thereby greatly improving the operating efficiency of the robot. The method can also play a role in improving position accuracy even if the robot works in a complicated and fickle environment. The proposed method applies an inertial measurement unit (IMU) and a position marker fixed on the robot tool to measure the robot's orientation and position. A hybrid filter, which integrates the unscented Kalman filter (UKF) and iterative particle filter (IPF), is developed to further improve the measurement accuracy of the robotic orientation and position, respectively. UKF is also adopted to reckon the errors of the robotic kinematic parameters in order to obtain better results. Particularly, the proposed method does not cover the complicated procedures, such as image capturing or laser tracking for improving the accuracy and efficiency of kinematic identification. To testify this method, a series of experiments have been performed. The experimental results indicate that the proposed method shows autonomy and precision in the robotic calibration process. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2020.2976277 | IEEE Transactions on Instrumentation and Measurement |
Keywords | DocType | Volume |
Hybrid filter,kinematic identification,multiple sensors,online robot calibration | Journal | 69 |
Issue | ISSN | Citations |
9 | 0018-9456 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guanglong Du | 1 | 115 | 14.06 |
| Yinhao Liang | 2 | 1 | 0.35 |
| Chunquan Li | 3 | 1 | 0.35 |
| Peter Xiaoping Liu | 4 | 1158 | 91.78 |
| Di Li | 5 | 305 | 16.43 |