Delay compensated state estimation for Telepresence robot navigation

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