Distributed Resilience-Aware Control in Multi-Robot Networks

2025-04-04

AI Summary

Malicious or faulty agents in multi-robot systems can compromise consensus attainment and induce collision risks. Method: This paper proposes a distributed resilient control framework that requires neither global state knowledge, fixed network topology, nor global communication. It integrates distributed control, resilient consensus theory, and time-varying graph modeling, relying solely on local neighbor information. Contribution/Results: We establish, for the first time, a sufficient condition for resilient consensus based on the local degrees of normal agents within time-varying networks. Crucially, we unify resilient consensus guarantees with real-time collision avoidance constraints within a Control Barrier Function (CBF) framework. The resulting controller ensures simultaneous asymptotic consensus convergence and provably safe navigation under partial agent failures. Extensive simulations demonstrate significant improvements in both system robustness and safety performance compared to existing approaches.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Ensuring resilient consensus in multi-robot systems with misbehaving agents remains a challenge, as many existing network resilience properties are inherently combinatorial and globally defined. While previous works have proposed control laws to enhance or preserve resilience in multi-robot networks, they often assume a fixed topology with known resilience properties, or require global state knowledge. These assumptions may be impractical in physically-constrained environments, where safety and resilience requirements are conflicting, or when misbehaving agents corrupt the shared information. In this work, we propose a distributed control law that enables each robot to guarantee resilient consensus and safety during its navigation without fixed topologies using only locally available information. To this end, we establish a new sufficient condition for resilient consensus in time-varying networks based on the degree of non-misbehaving or normal agents. Using this condition, we design a Control Barrier Function (CBF)-based controller that guarantees resilient consensus and collision avoidance without requiring estimates of global state and/or control actions of all other robots. Finally, we validate our method through simulations.

Problem

• Ensuring resilient consensus in multi-robot systems with misbehaving agents
• Overcoming impractical assumptions of fixed topology and global state knowledge
• Guaranteeing resilient consensus and safety using only local information

Innovation

• Distributed control law for resilient consensus
• Time-varying networks with local information
• CBF-based controller ensures safety