期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 65, 期 4, 页码 3337-3345出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2748058
关键词
Extended Kalman filter (EKF); non-Gauss noise; particle filter (PF); nonlinear system; robotic kinematic parameters calibration
资金
- National Natural Science Foundation of China [61733001, 61573063, 61503029]
- National High Technology Research Plan of China [2015AA043101, 2015BAF10B02]
- Basic Scientific Research of China [B2220133017]
Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly.
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