The Generalized Persistent Monitoring Problem
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2019 American Automatic Control Council. In this article, we consider the problem of planning an optimal route for an unmanned vehicle, tasked with persistently monitoring a set of targets. The targets are grouped into m subsets, referred to as clusters. To monitor a cluster, the vehicle must collect data from any one target in the cluster, by making a physical visit to the target. The vehicle has a finite fuel capacity, which is specified in terms of the number of visits it can make, at the end of which it must be refueled/recharged at a depot (which is one of the targets). Given k allowed visits for the vehicle, the problem of interest is to plan a closed walk (route) of k visits that can be repeated continuously, such that the maximum time between successive visits to the clusters is minimized (the minimum value is mathcal{R}-{ast}(k)). Here, we prove that for kgeq m-{2}-m, mathcal{R}-{ast}(k) takes only two values, mathcal{R}-{ast}(m) when k is an integral multiple of m, and mathcal{R}-{ast}(m+1) otherwise, leading to significant computational savings. We corroborate this result by performing numerical simulations on a Dubins vehicle.
