# Distributed Resilience-Aware Control in Multi-Robot Networks

arXiv: https://arxiv.org/abs/2504.03120 · HTML: https://mlsanity.com/paper/2504.03120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [Robust Multi-Agent LLMs under Byzantine Faults](https://mlsanity.com/paper/2605.09076/) (2026)
- [Fully Byzantine-Resilient Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2609.25701/) (2026)

## Abstract (arXiv)

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 share inaccurate state 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 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.