Dynamic Resilient Containment Control in Multirobot Systems

In this article, we study the dynamic resilient containment control problem for continuous-time multirobot systems (MRSs), i.e., the problem of designing a local interaction protocol that drives a set of robots, namely the followers, toward a region delimited by the positions of another set of robots, namely the leaders, under the presence of adversarial robots in the network. In our setting, all robots are anonymous, i.e., they do not recognize the identity or class of other robots. We consider as adversarial all those robots that intentionally or accidentally try to disrupt the objective of the MRS, e.g., robots that are being hijacked by a cyber–physical attack or have experienced a fault. Under specific topological conditions defined by the notion of (r,s)-robustness, our control strategy is proven to be successful in driving the followers toward the target region, namely a hypercube, in finite time. It is also proven that the followers cannot escape the moving containment area despite the persistent influence of anonymous adversarial robots. Numerical results with a team of 44 robots are provided to corroborate the theoretical findings

A Filtering Approach for Resiliency of Distributed Observers against Smart Spoofers

A network of observers is considered, where through asynchronous (with bounded delay) communications, they all estimate the states of a Linear Time-Invariant (LTI) system. In such setting, a new type of adversarial nodes might affect the observation process by impersonating the identity of the regular nodes, which is a violation against communication authenticity. These adversaries also inherit the capabilities of Byzantine nodes making them more powerful threats called smart spoofers. We show how asynchronous networks are vulnerable to smart spoofing attack. In the estimation scheme considered in this paper, information are flowed from the sets of source nodes, which can detect a portion of the state variables each, to the other follower nodes. The regular nodes, to avoid getting misguided by the threats, distributively filter the extreme values received from the nodes in their neighborhood. Topological conditions based on graph strong robustness are proposed to guarantee the convergence. Two simulation scenarios are provided to verify the results

Can Competition Outperform Collaboration? The Role of Misbehaving Agents

We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave. In contrast to commonly adopted filtering strategies, we draw inspiration from phenomena modeled through the Friedkin-Johnsen dynamics and argue that adding competition to the mix can improve resilience in the presence of misbehaving agents. Our intuition is corroborated by analytical and numerical results showing that (i) there exists a nontrivial trade-off between full collaboration and full competition and (ii) our competition-based approach can outperform state-of-the-art algorithms based on Weighted Mean Subsequence Reduced. We also study impact of communication topology and connectivity on resilience, pointing out insights to robust network design.Comment: Submitted to IEEE TAC - first revisio