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In this paper we consider a robot patrolling problem in which events arriverandomly over time at the vertices of a graph. When an event arrives it remainsactive for a random amount of time. If that time active exceeds a certainthreshold, then we say that the event is a true event; otherwise it is a falseevent. The robot(s) can traverse the graph to detect newly arrived events, andcan revisit these events in order to classify them as true or false. The goalis to plan robot paths that maximize the number of events that are correctlyclassified, with the constraint that there are no false positives. We show thatthe offline version of this problem is NP-hard. We then consider a simplepatrolling policy based on the traveling salesman tour, and characterize theprobability of correctly classifying an event. We investigate the problem whenmultiple robots follow the same path, and we derive the optimal (and notnecessarily uniform) spacing between robots on the path.

Robot Monitoring for the Detection and Confirmation of Stochastic Events