Adaptively Adjusted EKF-Based Magnet Tracking Method for Fast-Moving Object

Abstract

Permanent magnet tracking (PMT) can be employed in robots and precision control due to its advantage of wireless, without optical occlusion, and simultaneous position and orientation tracking. However, existing PMT studies based on optimization algorithms are limited to stationary or low-speed motions, where the tracking accuracy rapidly decreases with the increasing magnet speed. This study presents an extended Kalman filter (EKF)-based PMT. The magnetic field distribution around the cylindrical permanent magnet is approximated with a magnetic dipole model, where a random walk model is utilized as the system motion model. The state update model of the EKF estimation is adaptively adjusted according to different magnet speeds, while the initial magnet pose is approximated by the statistical analysis of magnetometer array outputs. The proposed PMT method hence can track the object at a higher speed while ensuring an effective pose accuracy than the previous studies. The proposed method was tested in technical verification experiments and a practical application. In addition, the relationship between the magnet speed and the position accuracy is investigated. When the magnet speed reaches 40 mm/s, experimental results show that the average pose error and algorithm latency are (1.83 ± 0.25 mm, 1.296 ± 0.091°) and 0.485 ± 0.064 ms, respectively. In the robot parking experiment for charging, the pose errors are (2.85 ± 0.41mm, 1.302 ± 0.023°) when the robot speed reaches 40 mm/s. The proposed method can greatly improve tracking performance for fast-moving object is greatly improved by performing the proposed method, thereby expanding the application scenarios of PMT.