Distributed Fault-Tolerant Multi-Robot Cooperative Localization in Adversarial Environments

TL;DR

This paper introduces a distributed, fault-tolerant cooperative localization method for multi-robot systems that maintains accuracy and communication efficiency in adversarial environments with sensor and communication disruptions.

Contribution

It presents a novel adaptive event-triggered communication strategy and analyzes its convergence and stability, enhancing robustness against adversarial attacks in multi-robot localization.

Findings

Significantly improves localization accuracy in adversarial conditions

Reduces communication load through adaptive strategies

Demonstrates robustness and scalability in real-world experiments

Abstract

In multi-robot systems (MRS), cooperative localization is a crucial task for enhancing system robustness and scalability, especially in GPS-denied or communication-limited environments. However, adversarial attacks, such as sensor manipulation, and communication jamming, pose significant challenges to the performance of traditional localization methods. In this paper, we propose a novel distributed fault-tolerant cooperative localization framework to enhance resilience against sensor and communication disruptions in adversarial environments. We introduce an adaptive event-triggered communication strategy that dynamically adjusts communication thresholds based on real-time sensing and communication quality. This strategy ensures optimal performance even in the presence of sensor degradation or communication failure. Furthermore, we conduct a rigorous analysis of the convergence and stability properties of the proposed algorithm, demonstrating its resilience against bounded adversarial zones and maintaining accurate state estimation. Robotarium-based experiment results show that our proposed algorithm significantly outperforms traditional methods in terms of localization accuracy and communication efficiency, particularly in adversarial settings. Our approach offers improved scalability, reliability, and fault tolerance for MRS, making it suitable for large-scale deployments in real-world, challenging environments.