提出分布式控制方法，让机器人在动态网络中无需全局信息即可保证共识与安全。
Distributed Resilience-Aware Control in Multi-Robot Networks
- 基于正常机器人数量设计分布式控制律，无需全局状态信息。
- 利用控制屏障函数实现共识与避障，支持时变拓扑结构。
- 适用于存在故障机器人、通信受限的复杂物理环境。
多机器人系统中确保对异常代理的弹性一致性仍是挑战，因现有网络弹性特性通常为组合性且全局定义。以往工作虽提出增强或保持弹性的控制律，但常假设固定拓扑或需全局状态知识，这在物理约束环境下不切实际，尤其当安全与弹性需求冲突，或异常代理提供错误状态信息时。本文提出一种分布式控制律，使每台机器人仅依赖局部信息即可在无固定拓扑条件下导航时保障弹性一致性和安全性。我们基于正常代理的度数建立了时变网络中弹性一致性的充分条件，并据此设计了基于控制屏障函数（CBF）的控制器，无需估计其他所有机器人的全局状态或控制动作，即可保证弹性一致性和碰撞避免。最后通过仿真验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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