Information fusion control with time delay for smooth pursuit eye movement

Abstract

Smooth pursuit eye movement depends on prediction and learning, and is subject to time delays in the visual pathways. In this paper, an information fusion control method with time delay is presented, implementing smooth pursuit eye movement with prediction and learning as well as solving the problem of time delays in the visual pathways. By fusing the soft constraint information of the target trajectory of eyes and the ideal control strategy, and the hard constraint information of the eye system state equation and the output equation, optimal estimations of the co‐state sequence and the control variable are obtained. The proposed control method can track not only constant velocity, sinusoidal target motion, but also arbitrary moving targets. Moreover, the absolute value of the retinal slip reaches steady state after 0.1 sec. Information fusion control method elegantly describes in a function manner how the brain may deal with arbitrary target velocities, how it implements the smooth pursuit eye movement with prediction, learning, and time delays. These two principles allowed us to accurately describe visually guided, predictive and learning smooth pursuit dynamics observed in a wide variety of tasks within a single theoretical framework. The tracking control performance of the proposed information fusion control with time delays is verified by numerical simulation results.

Gaze Point Tracking Based on a Robotic Body–Head–Eye Coordination Method

“…Avni et al [ 32 ] presented a framework for visual scanning and target tracking with a set of independent pan–tilt cameras based on model predictive control (MPC). In another study [ 33 ], the authors implemented smooth pursuit eye movement with prediction and learning in addition to solving the problem of time delays in the visual pathways. In addition, some saccade and smooth pursuit models have been validated on bionic eye systems [ 34 , 35 , 36 , 37 ].…”