Robust teleoperation in a non-visible environment with a new prediction scheme

We propose a new prediction scheme for robust teleoperation in a non-visible environment. The positioning error caused by the time delay in a non-visible environment was compensated for by the Smith predictor, and the sensory data was estimated by the grey model. The Smith predictor was effective in compensating for the positioning error caused by the time delay with a precise system model. Therefore, a dynamic model of a mobile robot was derived in this research. To minimize the unstable and erroneous states caused by the time delay, the estimated sensor data were sent to the operator. Through simulations, the possibility of compensating the errors caused by the time delay was verified using the Smith predictor. In addition, the estimation reliability of the measurement data has been demonstrated. Robust teleoperations in a non-visible environment have been performed with a mobile robot to avoid obstacles and move to the target position by the proposed prediction scheme, which combines the Smith predictor with the grey model. Although a human operator is involved in the teleoperation loop, the compensation effects have been demonstrated