Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game.

翻译：去中心化控制方案因计算效率高且适用于大规模系统，在涉及多智能体系统的诸多领域中日益受到青睐。然而，缺乏显式全局协调者时，分布式智能体难以确定如何高效与其他智能体交互。本文提出一种基于风险感知的去中心化控制框架，该框架能为单个智能体在无需直接通信的情况下，提供在高效运动时需承担多少相对责任份额（百分比）以避免碰撞的指导。我们设计了一种新颖的基于控制障碍函数（CBF）的风险度量，用于刻画运动不确定性下智能体面临潜在碰撞的聚合风险。利用该度量动态分配智能体间的责任份额，并开发了基于风险感知的去中心化安全控制器。通过此方法，我们能够利用低风险机器人的灵活性，提升高风险机器人的运动灵活性，从而增强整体安全性。通过高速公路匝道合流与多智能体位姿交换博弈两个实例，验证了所提方法的有效性与高效性。