提出分布式风险敏感安全滤波器，提升不确定多智能体系统的安全性与鲁棒性。
Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
- 基于价值函数定义的控制屏障函数，结合指数风险算子实现风险敏感安全约束。
- 通过最坏情况预判与安全策略邻近性两种策略切换，保障可行性与安全性。
- 适用于需去中心化协调的不确定离散系统，适合复杂动态环境中的安全控制设计。
在难以实现集中协调的多智能体系统中，确保安全性是一项重大挑战。本文针对具有不确定动力学的离散时间多智能体系统，提出一种新型风险敏感安全滤波器，其通过价值函数定义控制屏障函数（CBFs）。该方法依赖于基于指数风险算子的集中式风险敏感安全条件，以增强对模型不确定性下的鲁棒性。我们推导出两种替代的分布式实现策略：一种基于最坏情况预判，另一种基于接近已知安全策略的邻近性。允许智能体在策略间切换可保证可行性。通过详尽的数值实验，验证了该方法在不过度保守的前提下有效维持系统安全。
原文摘要 · Abstract (English)
Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sensitive safety filter for discrete-time multi-agent systems with uncertain dynamics that leverages control barrier functions (CBFs) defined through value functions. Our approach relies on centralized risk-sensitive safety conditions based on exponential risk operators to ensure robustness against model uncertainties. We introduce a distributed formulation of the safety filter by deriving two alternative strategies: one based on worst-case anticipation and another on proximity to a known safe policy. By allowing agents to switch between strategies, feasibility can be ensured. Through detailed numerical evaluations, we demonstrate the efficacy of our approach in maintaining safety without being overly conservative.
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