Study of the impact of delay on human remote navigators with application to receding horizon control

This paper examines the impact of delays on human performance and human strategies when remotely navigating autonomous vehicles, and develops a robust human inspired delay compensation. Vehicles chosen for the study are ground autonomous vehicles which are allowed to stop, providing an instrumental feature that enables it to capture some important human behavior. The effects of delay on human behavior when remotely navigating autonomous vehicles have been captured by a nonlinear model predictive (also known as receding horizon) controller. This study provides some insights into designing human in-the-loop systems for remote navigation of autonomous vehicles when the delays are not negligible. We offer a human inspired strategy for dealing with delay in a fully autonomous receding horizon controller which we show to be safe and convergent for bounded delays.

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