发表机构
DIAG, Sapienza University of Rome; ISTC, National Research Council; Centre for the Advanced Study of Collective Behaviour, University of Konstanz; Department of Computer and Information Science, University of Konstanz; Max Planck Institute of Animal Behavior（罗马第一大学诊断与生物医学信息系; 意大利国家研究委员会信息科学与技术研究所; 康斯坦茨大学集体行为高级研究中心; 康斯坦茨大学计算机与信息科学系; 马克斯·普朗克动物行为研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究机器人集群匿名通信下群体感应问题，引入受k优先级采样启发的随机滤波协议\ANTk，与基线协议\AN及变体\ANT比较，发现\AN简洁但不准确，\ANT变体提高准确性但收敛慢，\ANTk可减少误差稳定估计但恢复时间增加。
AI 中文摘要
群体感应（QS）是机器人集群的关键能力，有助于群体层面的活动协调。有效通信对个体估计整个集群的群体水平至关重要。匿名通信协议可通过邻居信息采样支持群体估计并保持QS过程的可扩展性，但因无法区分消息源，会使同一发送者的重复消息被重复计算，导致群体估计有偏差。本研究引入受k优先级采样启发的随机滤波协议（\ANTk）来提高估计稳定性，并与基线匿名协议（\AN）和旨在提高准确性的随机变体（\ANT）比较。发现基线协议\AN简洁快速但因重复计数偏差不准确；\ANT变体提高了准确性但有信息惯性，收敛慢。最后，通过\ANTk协议主动过滤消息缓冲区成功减少了临时误差并稳定了估计，但恢复误差的时间增加。
英文摘要
Quorum Sensing (QS) is a key capability for robot swarms, useful for coordination of activities at the group level. Effective communication is instrumental for individuals to estimate the quorum level of the entire swarm. Anonymous communication protocols where individuals exchange local information without revealing unique identities are helpful to support quorum estimates by sampling information from neighbours and maintain scalability of the QS process. However, because anonymous protocols cannot distinguish message sources, repeated messages from the same sender may be double-counted, thereby biasing collective quorum estimates. In this study, we introduce a stochastic filtering protocol inspired by $k$-priority sampling to improve estimate stability (\ANTk), and we compare it with a baseline anonymous protocols (\AN) and a randomised variant designed to improve accuracy (\ANT). We find that the baseline protocol \AN provides a parsimonious and fast solution, but remains highly inaccurate due to double-counting bias. The \ANT variant improves accuracy but suffers from information inertia, resulting in slower convergence. Finally, actively filtering the message buffer via the \ANTk protocol successfully decreases temporary errors and stabilises the estimate, at the cost of an increased time of recovery from errors.
