Adaptive Model-Mediated Teleoperation for Tasks Interacting With Uncertain Environment

Abstract

Model-mediated teleoperation (MMT) employs an environment model at the master side to compute feedback output to the master at a faster rate. This approach improves system stability in the presence of time delay. MMT, however, does not generally perform well if the employed model is not accurate. The model mismatch is unavoidable when the environment is unknown in advance or varies. This paper proposes MMT employing an adaptive model. The proposed method adaptively moves the reference point of the employed model, whereas the previous MMTs used reference points fixed to the surface of objects in the environment. This can make system stability independent of the time delay. Experiments show that the proposed method improves stability compared to the previous MMTs when there are model mismatches. User studies are conducted to compare the operator’s performance in two tasks, control of force exerted to objects in the environment, and discrimination of object stiffness. The result shows that the error in the forces applied to objects in the environment significantly decreases in the proposed method. Errors in forces rendered to the master are also improved by at least 20.2%. The experiment result also shows that subjects can discriminate up to 40.9% smaller differences in the stiffness than the previous MMT under the same time delay.