| Summary: | We present the development of computational models of biological motor control used in two different types of eye movements --- gaze shifting and gaze stabilization. They are then implemented and tested on robotic systems. The thesis also investigates the application of these biological motor control strategies in robotics applications.
We describe and test a non-linear control algorithm inspired by the behaviour of motor neurons in humans during extremely fast saccadic eye movements involved in gaze shifting. The algorithm is implemented on a robotic eye connected with a stiff camera cable, similar to the optic nerve. This adds a complicated non-linear stiffness to the plant. For high speed movement, our "pulse-step" controller operates in open-loop using an internal model of the eye plant learned from past measurements. We show that the controller approaches the performance seen in the human eye, producing fast movements with little overshoot. Interestingly, the controller reproduces the main sequence relationship observed in animal eye movements.
We also model the control of eye movements that serve to stabilize its gaze direction. To test and evaluate this stabilization algorithm, we use a camera mounted on a robotic test platform that can have unknown perturbations in the horizontal plane. We show that using models of the vestibulo-ocular and optokinetic reflexes to control the camera allows the camera to be effectively stabilized using an inertial sensor and a single additional motor, without the need for a joint position sensor. The algorithm uses an adaptive controller based on a model of the vertebrate Cerebellum for velocity stabilization, with additional drift correction. A resolution-adaptive retinal slip algorithm that is robust to motion blur was also developed. We show that the resulting system can reduce camera image motion to about one pixel per frame on average even when the platform is rotated at 200 degrees per second. As a practical robotic application, we also demonstrate how the common task of face detection benefits from active gaze stabilization.
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