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.

翻译：多机器人系统的一项重要实际应用是多机器人巡逻（MRP），即机器人需定期穿越某个区域。MRP的动机包括检测可能构成安全威胁的异常事件。尽管MRP算法在开发上已相对成熟，但其一个关键潜在优势尚未得到充分探究：利用对检测到异常的集体感知来优先安排安全检查位置顺序的能力。这是因为个体层面存在噪声的异常检测，可能通过群体层面的共识形成来弥补——即关于某个异常是否确实存在的判断。本文研究了未经改进的基于空闲度的巡逻算法在附加目标（即通过局部成对通信和法定阈值达成环境感知共识）下的性能。我们发现，通常而言，能促进机器人物理混合（以其涌现通信网络的较高连通性衡量）的MRP算法能更快达成共识。然而，当异常检测中存在噪声时，适度（受限）的连通性更为可取，因为它能减少误检的传播（以群体层面的F值衡量）。这些发现可为用户选择MRP算法及未来算法开发提供指导。