Distributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems

TL;DR

This paper introduces a novel safety framework for multi-agent systems using distributed predictive control barrier functions, ensuring safety despite topology changes and disturbances.

Contribution

It develops structured control barrier functions integrated into a predictive safety layer, enabling scalable, formal safety guarantees in dynamic multi-agent networks.

Findings

The approach guarantees safety recovery during topology changes.

Simulations and experiments validate the effectiveness of the safety framework.

The method supports plug-and-play agent addition or removal.

Abstract

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.