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

2025-07-09

AI Summary

This work addresses collaborative localization of multi-robot systems under GPS denial, limited communication bandwidth, and adversarial attacks—including sensor spoofing and channel interference. We propose a distributed fault-tolerant cooperative localization framework. Our method introduces an adaptive event-triggered communication protocol that dynamically adjusts transmission frequency based on real-time perception uncertainty and communication quality; integrates online channel assessment with bounded adversarial modeling to ensure convergence and estimation accuracy under malicious interference; and provides rigorous theoretical guarantees on stability and fault tolerance within bounded adversarial regions. Experiments on the Robotarium platform demonstrate that the proposed approach achieves a 32% reduction in average localization error and a 47% reduction in communication bandwidth usage compared to conventional methods, while exhibiting strong robustness against diverse attacks and excellent scalability to larger robot teams.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Enhancing resilience in multi-robot localization against adversarial attacks
• Adaptive communication strategy for sensor and network disruptions
• Ensuring accurate state estimation in adversarial and GPS-denied environments

Innovation

• Distributed fault-tolerant cooperative localization framework
• Adaptive event-triggered communication strategy
• Convergence and stability analysis against adversarial zones