  Some human-machine systems are designed so that machines (robots) gather and
deliver data to remotely located operators (humans) through an interface in
order to aid them in classification. The performance of a human as a (binary)
classifier-in-the-loop is characterized by probabilities of correctly
classifying objects of type $T$ and $F$. These two probabilities depend on the
dwell time, $d$, spent collecting information at a point of interest (POI or
interchangeably, target). The information gain associated with collecting
information at a target is then a function of dwell time $d$ and discounted by
the revisit time, $R$, i.e., the duration between consecutive revisits to the
same target. The objective of the problem of routing for classification is to
optimally route the vehicles and determine the optimal dwell time at each
target so as to maximize the total discounted information gain while visiting
every target at least once. In this paper, we make a simplifying assumption
that the information gain is discounted exponentially by the revisit time; this
assumption enables one to decouple the problem of routing with the problem of
determining optimal dwell time at each target for a single vehicle problem. For
the multi-vehicle problem, we provide a fast heuristic to obtain the allocation
of targets to each vehicle and the corresponding dwell time.

Optimization and Control