| Summary: | Following a paper published by E. Boje[1], this thesis discusses the design and off-line testing of different types of Kalman filters to estimate the attitude, position and velocity of a robotic platform moving along a power line. The nature of this problem limits the use of magnetometers. Magnetic field interference from the steel pylons and steel cored conductors will affect the local magnetic field. Moreover, high frequency signals from on-board power electronic drives and induced magnetic fields due to ferromagnetic components of the robot along with aliasing, quantization effects and a low signal to noise ratio make notch filtering at 50 Hz impractical. Thus, a GPS/IMU filter solution, which uses the power line curvature and horizontal direction in measurements, to constrain the robot to the line was designed. Different types of filters were implemented; The Extended Kalman filter (EKF), the Unscented Kalman filter (UKF) and the Error State Kalman filter (ErKF). Measurements were recorded and the filters were tested offline. While all the filters tracked properly, it was found that the EKF was better in computational speed completing an iteration in 87 µs, the ErKF was second best with an average time of 120 µs for one iteration and the UKF was last with an average time of 1040 µs for one iteration. Errors between the true state and estimated state for the simulation were quantified using root mean square values (RMS). The RMS values were almost the same for the EKF and ErKF with the error for the x position at 0.81 m and z position at 0.038 m. The UKF produced RMS errors of 0.79 m for x position and 0.11 m for z position. It can be seen that the UKF is slightly better for the x position but is much worse for the z position. Overall, the GPS measurement RMS values used were 4 m and 20 m for the horizontal and vertical positions respectively. Thus, the filters brought a big improvement. However, the recommended filter is the EKF as is produced comparable or better results as compared to other filters and expends the least computational effort. A state estimator was also developed for a J.Patel’s PLIR project [2], where a brachiating version of a power line robot was modeled. The brachiation mechanism was approximated to a double pendulum and kinematics based Kalman filter was designed. Simulations of EKF and UKF were made. The EKF is still recommended as its estimates are closer to the true values and its computation time is about five times faster.
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