Trust networks in multi-robot communities

Mantel, Kevin T.; Clark, Christopher M.

doi:10.1109/robio.2012.6491281

Cited by 11 publications

(2 citation statements)

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“…One way to protect against the damage of misinformation, that we will employ in this paper, is by understanding trust for certain robots in the network [35]. An example of how this can be achieved is by sensing anomalies in the information received by malicious robots [36].…”

Section: Related Workmentioning

confidence: 99%

“…Communication links between robots are shown in yellow. more avenues for verification of the data that cannot be easily manipulated by an adversary [20,21,22,23,24,25]. For example, previous work shows that physical information in the network can be used to detect trustworthiness in the presence of certain attacks such as a Sybil Attack [19,26,27] where an adversary "spoofs", or spawns, another robot into the system to gain a disproportionate influence on the group decision [28].…”

Section: Introductionmentioning

confidence: 99%

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Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking

Mallmann-Trenn¹,

Cavorsi²,

Gil³

2020

Preprint

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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, FindSpoofedRobots that determines trusted neighbors with high probability, and FindResilientAdjacency-Matrix 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.

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Section: Related Workmentioning

confidence: 99%