Auction-Based Responsibility Allocation for Scalable Decentralized Safety Filters in Cooperative Multi-Agent Collision Avoidance
Johannes Autenrieb, Mark Spiller
TL;DR
The work addresses the scalability challenge of decentralized safety filters in cooperative multi-agent collision avoidance by combining high-order control barrier functions (HOCBFs) with an auction-based responsibility allocation. Safety is maintained through neighborhood-based, event-triggered HOCBF constraints evaluated by each agent using local information and cooperative/limited-communication assumptions, solved via a local QP over active constraints. To scale, a distributed auction assigns responsibility for enforcing safety constraints across agents through a directed graph, minimizing collective corrective effort while ensuring coverage for every active interaction. Simulations across 3, 8, and 20 agents show safe operation with substantially reduced per-agent computational load and robust performance in dense networks, highlighting the approach's practical potential for scalable multi-agent coordination.
Abstract
This paper proposes a scalable decentralized safety filter for multi-agent systems based on high-order control barrier functions (HOCBFs) and auction-based responsibility allocation. While decentralized HOCBF formulations ensure pairwise safety under input bounds, they face feasibility and scalability challenges as the number of agents grows. Each agent must evaluate an increasing number of pairwise constraints, raising the risk of infeasibility and making it difficult to meet real-time requirements. To address this, we introduce an auction-based allocation scheme that distributes constraint enforcement asymmetrically among neighbors based on local control effort estimates. The resulting directed responsibility graph guarantees full safety coverage while reducing redundant constraints and per-agent computational load. Simulation results confirm safe and efficient coordination across a range of network sizes and interaction densities.