State Prediction for Haptic Remote Teleoperation - A Kalman Filter ApproachState Prognos för haptisk Remote teleoperation – en metod baserad på Kalman-filter

Abstract: Teleoperation system is an important tool to control a device or model in an isolated area remotely where the operator cannot perform the task locally. The vast majority of teleoperation systems provides the operator with visual and haptic control to accomplish the assignment as naturally as possible. However, on a teleoperation system with considerable distance, the time delay could cause a drop in performance. This thesis aims to minimize delay problem by implementing a prediction approach using Kalman Filter. Kalman Filter algorithm has been widely used to estimate user movement for tracking systems. Kalman filter provides an efficient mechanism to predict future state based on Bayesian estimation to sequentially predict future states and measure an actual system to update system parameters. The primary objective of this work is to extract information generated by our prototyping model and visualizing the data to reflect the performance of the system. We use Phantom Omni devices and 3D arm as a model. Different type of Kalman filter algorithms is used to test the accuracy and performance of predicted state generated by the filter. The result shows that the implementation of Extended Kalman Filter (EKF) and smoothing function could overcome the networking delay on certain degrees. The comparison shows that the EKF has better accuracy and performance compared to Unscented Kalman Filter (UKF) when estimating the future state. Additionally, the implementation of smoothing function could improve the stability of teleoperation system.