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

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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.
Original languageEnglish
Title of host publicationSAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9798400702433
Publication statusPublished - 21 May 2024
EventThe 39th ACM/SIGAPP Symposium On Applied Computing - University of Salamanca, Avila, Spain
Duration: 8 Apr 202412 Apr 2024


ConferenceThe 39th ACM/SIGAPP Symposium On Applied Computing
Abbreviated titleSAC '24
Internet address


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