  In robotic systems, perception latency is a term that refers to the computing
time measured from the data acquisition to the moment in which perception
output is ready to be used to compute control commands. There is a compromise
between perception latency, precision for the overall robotic system, and
computational resource usage referred to here as the latency-precision
trade-off. In this work, we analyze a robot model given by a linear system, a
zero-order hold controller, and measurements taken by several perception mode
possibilities with different noise levels. We show that the analysis of this
system is reduced to studying an equivalent switching system. Our goal is to
schedule perception modes such that stability is attained while optimizing a
cost function that models the latency-precision trade-off. Our solution
framework comprises three main tools: the construction of perception scheduling
policy candidates, admissibility verification for policy candidates, and
optimal strategies based on admissible policies.
