Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking

TY - JOUR

T1 - Crowd Vetting

T2 - Rejecting Adversaries via Collaboration With Application to Multirobot Flocking

AU - Mallmann-Trenn, Frederik

AU - Cavorsi, Matthew

AU - Gil, Stephanie

N1 - Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - In this article, 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 their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms, FindSpoofedRobots that determines trusted neighbors with high probability, and 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.

AB - In this article, 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 their neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize the number of communication rounds required to be a function of the communication quality and of the proportion of legitimate to malicious robots. This result enables increased resiliency of many multirobot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems of a time critical nature. We develop two algorithms, FindSpoofedRobots that determines trusted neighbors with high probability, and 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.

KW - Collision avoidance

KW - Distributed robot systems

KW - multirobot systems

KW - networked robots

KW - Resilience

KW - resilient coordination

KW - Robot kinematics

KW - Robots

KW - Social networking (online)

KW - Target tracking

KW - Vehicle dynamics

UR - http://www.scopus.com/inward/record.url?scp=85110814019&partnerID=8YFLogxK

U2 - 10.1109/TRO.2021.3089033

DO - 10.1109/TRO.2021.3089033

M3 - Article

AN - SCOPUS:85110814019

SN - 1552-3098

JO - IEEE TRANSACTIONS ON ROBOTICS

JF - IEEE TRANSACTIONS ON ROBOTICS

ER -