为模块化多智能体系统设计可扩展的安全认证框架
Distributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems
- 用预测优化方法构建分布式安全层，动态保障安全
- 支持智能体随时加入或退出网络，仍能恢复安全
- 适用于需实时安全保证的自动驾驶车队等场景
针对具有分布式控制架构且网络拓扑可能变化的安全关键型多智能体系统，现有基于学习的分布式控制虽具可扩展性和高性能，但在面对未预见干扰和网络拓扑突变时缺乏形式化安全保证，可能导致系统失效。为此，本文提出结构化控制屏障函数（s-CBFs），并进一步构建分布式预测控制屏障函数（D-PCBF），一种基于模型预测的、优化驱动的安全层，可在任意时刻确保系统的可恢复性安全。该方法实现了一种宽松但形式化的即插即用协议，允许智能体在不破坏安全的前提下自由加入或离开网络，即使拓扑变更导致暂时不安全行为，也能保证安全恢复。通过微型赛车队的仿真与实时实验验证了该方法的有效性。
原文摘要 · Abstract (English)
We consider safety-critical multi-agent systems with distributed control architectures and potentially varying network topologies. While learning-based distributed control enables scalability and high performance, a lack of formal safety guarantees in the face of unforeseen disturbances and unsafe network topology changes may lead to system failure. To address this challenge, we introduce structured control barrier functions (s-CBFs) as a multi-agent safety framework. The s-CBFs are augmented to a distributed predictive control barrier function (D-PCBF), a predictive, optimization-based safety layer that uses model predictions to guarantee recoverable safety at all times. The proposed approach enables a permissive yet formal plug-and-play protocol, allowing agents to join or leave the network while ensuring safety recovery if a change in network topology requires temporarily unsafe behavior. We validate the formulation through simulations and real-time experiments of a miniature race-car platoon.
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