  We design persistent surveillance strategies for the quickest detection of
anomalies taking place in an environment of interest. From a set of predefined
regions in the environment, a team of autonomous vehicles collects noisy
observations, which a control center processes. The overall objective is to
minimize detection delay while maintaining the false alarm rate below a desired
threshold. We present joint (i) anomaly detection algorithms for the control
center and (ii) vehicle routing policies. For the control center, we propose
parallel cumulative sum (CUSUM) algorithms (one for each region) to detect
anomalies from noisy observations. For the vehicles, we propose a stochastic
routing policy, in which the regions to be visited are chosen according to a
probability vector. We study stationary routing policy (the probability vector
is constant) as well as adaptive routing policies (the probability vector
varies in time as a function of the likelihood of regional anomalies). In the
context of stationary policies, we design a performance metric and minimize it
to design an efficient stationary routing policy. Our adaptive policy improves
upon the stationary counterpart by adaptively increasing the selection
probability of regions with high likelihood of anomaly. Finally, we show the
effectiveness of the proposed algorithms through numerical simulations and a
persistent surveillance experiment.
