  An important real-world application of multi-robot systems is multi-robot
patrolling (MRP), where robots must carry out the activity of going through an
area at regular intervals. Motivations for MRP include the detection of
anomalies that may represent security threats. While MRP algorithms show some
maturity in development, a key potential advantage has been unexamined: the
ability to exploit collective perception of detected anomalies to prioritize
the location ordering of security checks. This is because noisy
individual-level detection of an anomaly may be compensated for by group-level
consensus formation regarding whether an anomaly is likely to be truly present.
Here, we examine the performance of unmodified idleness-based patrolling
algorithms when given the additional objective of reaching an environmental
perception consensus via local pairwise communication and a quorum threshold.
We find that generally, MRP algorithms that promote physical mixing of robots,
as measured by a higher connectivity of their emergent communication network,
reach consensus more quickly. However, when there is noise present in anomaly
detection, a more moderate (constrained) level of connectivity is preferable
because it reduces the spread of false positive detections, as measured by a
group-level F-score. These findings can inform user choice of MRP algorithm and
future algorithm development.
