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An important real-world application of multi-robot systems is multi-robotpatrolling (MRP), where robots must carry out the activity of going through anarea at regular intervals. Motivations for MRP include the detection ofanomalies that may represent security threats. While MRP algorithms show somematurity in development, a key potential advantage has been unexamined: theability to exploit collective perception of detected anomalies to prioritizethe location ordering of security checks. This is because noisyindividual-level detection of an anomaly may be compensated for by group-levelconsensus formation regarding whether an anomaly is likely to be truly present.Here, we examine the performance of unmodified idleness-based patrollingalgorithms when given the additional objective of reaching an environmentalperception 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 anomalydetection, a more moderate (constrained) level of connectivity is preferablebecause it reduces the spread of false positive detections, as measured by agroup-level F-score. These findings can inform user choice of MRP algorithm andfuture algorithm development.

Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus