Coordination in a large number of networked robots is a challenging task, especially when robots are constantly moving around the environment and there are malicious attacks within the network. Various approaches in the literature exist for detecting malicious robots, such as message sampling or suspicious behavior analysis. However, these approaches require every robot to sample every other robot in the network, leading to a slow detection process that degrades team performance. This paper introduces a method that significantly decreases the detection time for legitimate robots to identify malicious robots in a scenario where legitimate robots are randomly moving around the environment. Our method leverages the concept of ``Dynamic Crowd Vetting" by utilizing observations from random encounters and trusted neighboring robots' opinions to quickly improve the accuracy of detecting malicious robots. The key intuition is that as long as each legitimate robot accurately estimates the legitimacy of at least some fixed subset of the team, the second-hand information they receive from trusted neighbors is enough to correct any misclassifications and provide accurate trust estimations of the rest of the team. We show that the size of this fixed subset can be characterized as a function of fundamental graph and random walk properties. Furthermore, we formally show that as the number of robots in the team increases the detection time remains constant. We develop a closed form expression for the critical number of time-steps required for our algorithm to successfully identify the true legitimacy of each robot to within a specified failure probability. Our theoretical results are validated through simulations demonstrating significant reductions in detection time when compared to previous works that do not leverage trusted neighbor information.

翻译：大量网络化机器人的协调是一项具有挑战性的任务，尤其是在机器人持续在环境中移动且网络中存在恶意攻击的情况下。现有文献提出了多种检测恶意机器人的方法，例如消息采样或可疑行为分析。然而，这些方法要求每个机器人对网络中的其他所有机器人进行采样，导致检测过程缓慢，从而降低团队性能。本文引入了一种方法，在合法机器人在环境中随机移动的场景下，显著缩短了合法机器人识别恶意机器人的检测时间。我们的方法利用"动态人群审查"的概念，通过利用随机相遇的观测结果和受信任相邻机器人的意见，快速提高检测恶意机器人的准确性。其关键直觉在于：只要每个合法机器人能够准确估计团队中至少某个固定子集的合法性，它们从受信任邻居处获得的二手信息就足以纠正任何错误分类，并对团队其余成员提供准确的信任估计。我们证明，该固定子集的大小可以用基础图论和随机游走性质的函数来表征。此外，我们形式化地证明，随着团队中机器人数量增加，检测时间保持不变。我们为算法成功识别每个机器人真实合法性所需的关键时间步数（在指定失败概率内）推导出闭合表达式。通过仿真验证了我们的理论结果，与未利用受信任邻居信息的先前工作相比，检测时间显著减少。