In this dissertation, we present results from a study on the performance of humans and automatic controllers in a general remote navigation task. The remote navigation task is defined as driving a vehicle with nonholonomic kinematic constraints around obstacles toward a goal. We conducted experiments with humans and automatic controllers; in these experiments, the number and type of obstacles as well as the feedback delay was varied. Humans showed significantly more robust performance compared to that of a receding horizon controller. Using the human data, we then train a new human-like receding horizon controller which provides goal convergence when there is no uncertainty. We show that paths produced by the trained human-like controller are similar to human paths and that the trained controller improves robustness compared to the original receding horizon controller. We also show the impact of feedback delay on five metrics of human operator performance and for each metric characterize the impact as linear or superlinear. Finally we propose a human-inspired strategy for the automatic controller to robustly handle feedback delay.
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