Mean-Field Control Barrier Functions: A Framework for Real-Time Swarm Control

TL;DR

This paper introduces Mean-field Control Barrier Functions (MF-CBFs), extending traditional CBFs to large-scale swarm control by modeling agents as probability measures, enabling real-time safety guarantees in multi-agent systems.

Contribution

It develops a novel MF-CBF framework that applies control barrier functions to mean-field models, addressing computational challenges in large multi-agent systems.

Findings

MF-CBFs enable safety guarantees in swarm control.

The framework relies on differential calculus in probability measure spaces.

Potential for real-time application in large-scale multi-agent systems.

Abstract

Control Barrier Functions (CBFs) are an effective methodology to ensure safety and performative efficacy in real-time control applications such as power systems, resource allocation, autonomous vehicles, robotics, etc. This approach ensures safety independently of the high-level tasks that may have been pre-planned offline. For example, CBFs can be used to guarantee that a vehicle will remain in its lane. However, when the number of agents is large, computation of CBFs can suffer from the curse of dimensionality in the multi-agent setting. In this work, we present Mean-field Control Barrier Functions (MF-CBFs), which extends the CBF framework to the mean-field (or swarm control) setting. The core idea is to model a population of agents as probability measures in the state space and build corresponding control barrier functions. Similar to traditional CBFs, we derive safety constraints on the (distributed) controls but now relying on the differential calculus in the space of probability measures.