Delay compensated state estimation for Telepresence robot navigation

Abstract

Telepresence robots empower human operators to navigate remote environments. However, operating and navigating the robot in an unknown environment is challenging due to delay in the communication network (e.g., distance, bandwidth, communication drop-outs etc.), processing delays and slow dynamics of the mobile robots resulting in time-lagged in the system. Also, erroneous sensor data measurement which is important to estimate the robot’s true state (positional information) in the remote environment, often create complications and make it harder for the system to control the robot. In this paper, we propose a new approach for state estimation assuming uncertain delayed sensor measurements of a Telepresence robot during navigation. A new real world experimental model, based on Augmented State Extended Kalman Filter (AS-EKF), is proposed to estimate the true position of the Telepresence robot. The uncertainty of the delayed sensor measurements have been modelled using probabilistic density functions (PDF). The proposed model was successfully verified in our proposed experimental framework which consists of a state-of-the-art differential-drive Telepresence robot and a motion tracking multi-camera system. The results show significant improvements compared to the traditional EKF that does not consider uncertain delays in sensor measurements. The proposed model will be beneficial to build a real time predictive display by reducing the effect of visual delay to navigate the robot under the operator’s control command, without waiting for delayed sensor measurements.