Combinatorial Safety-Critical Coordination of Multi-Agent Systems via Mixed-Integer Responsibility Allocation and Control Barrier Functions

TL;DR

This paper introduces a hybrid coordination framework for multi-agent systems that combines control barrier functions with a mixed-integer responsibility allocation to improve safety and efficiency in dense environments.

Contribution

It proposes a novel combinatorial coordination layer using MILP to assign collision responsibilities, reducing redundancy and computational load.

Findings

Reduces redundant safety constraints among agents.

Improves computational efficiency in multi-agent safety enforcement.

Ensures formal safety guarantees with distributed responsibility allocation.

Abstract

This paper presents a hybrid safety-critical coordination architecture for multi-agent systems operating in dense environments. While control barrier functions (CBFs) provide formal safety guarantees, decentralized implementations typically rely on ego-centric safety filtering and may lead to redundant constraint enforcement and conservative collective behavior. To address this limitation, we introduce a combinatorial coordination layer formulated as a mixed-integer linear program (MILP) that assigns collision-avoidance responsibilities among agents. By explicitly distributing enforcement tasks, redundant reactions are eliminated and computational complexity is reduced. Each agent subsequently solves a reduced local quadratic program enforcing only its assigned constraints.