群衆検証：協調による敵対ロボットの排除とマルチロボット群れ制御への応用
Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking
ロボットが近隣の意見を活用してシビル攻撃下の敵対ロボットを高い確率で検出・排除する手法を提案し、移動目標追従の群れ制御に応用して耐障害性を向上させた。
著者: Frederik Mallmann-Trenn, Matthew Cavorsi, Stephanie Gil
分類: cs.RO
原文アブストラクト
We characterize the advantage of using a robot's neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of its neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize a number of communication rounds required to achieve this result to be a function of the communication quality and the proportion of legitimate to malicious robots. This result enables increased resiliency of many multi-robot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems with a time critical nature. We develop two algorithms, \emph{FindSpoofedRobots} that determines trusted neighbors with high probability, and \emph{FindResilientAdjacencyMatrix} that enables distributed computation of graph properties in an adversarial setting. We apply our methods to a flocking problem where a team of robots must track a moving target in the presence of adversarial robots. We show that by using our algorithms, the team of robots are able to maintain tracking ability of the dynamic target.
