Distributed Detection of Adversarial Attacks for Resilient Cooperation of Multi-Robot Systems with Intermittent Communication

TL;DR

This paper develops a distributed framework for detecting malicious agents in multi-robot systems with intermittent communication, ensuring resilient consensus and formation despite adversarial attacks and network disruptions.

Contribution

It introduces explicit connectivity bounds and a reconfigurable detection method with theoretical guarantees for resilient multi-robot cooperation under adversarial and communication challenges.

Findings

System achieves finite-gain $ ext{L}_p$ stability and exponential consensus with malicious agent detection.

Explicit bounds for network connectivity ensure resilience against specific attacks.

Simulation validates the effectiveness of the proposed detection and cooperation framework.

Abstract

This paper concerns the consensus and formation of a network of mobile autonomous agents in adversarial settings where a group of malicious (compromised) agents are subject to deception attacks. In addition, the communication network is arbitrarily time-varying and subject to intermittent connections, possibly imposed by denial-of-service (DoS) attacks. We provide explicit bounds for network connectivity in an integral sense, enabling the characterization of the system's resilience to specific classes of adversarial attacks. We also show that under the condition of connectivity in an integral sense uniformly in time, the system is finite-gain $\mathcal{L}_{p}$ stable and uniformly exponentially fast consensus and formation are achievable, provided malicious agents are detected and isolated from the network. We present a distributed and reconfigurable framework with theoretical guarantees for detecting malicious agents, allowing for the resilient cooperation of the remaining cooperative agents. Simulation studies are provided to illustrate the theoretical findings.