Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems

2025-06-09

AI Summary

This paper addresses the challenge of safety-critical control for uncertain discrete-time multi-agent systems operating without centralized coordination. Method: We propose a distributed risk-sensitive safety filtering framework. Its core components are: (1) a novel Control Barrier Function (CBF) defined via a value function and integrated with an exponential risk operator, yielding provably safe distributed safety conditions; and (2) two switchable mechanisms—worst-case anticipation and safety-policy proximity—that dynamically balance robustness and feasibility. The approach relies solely on local communication and neighbor state estimates, requiring no global information or central coordinator. Contribution/Results: Numerical experiments demonstrate that the framework strictly satisfies safety constraints while significantly reducing the conservatism inherent in conventional CBF-based methods, enabling real-time, decentralized safety filtering.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

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.

Problem

• Ensuring safety in multi-agent systems with uncertain dynamics
• Developing risk-sensitive safety filters using control barrier functions
• Distributed strategies for robust safety without centralized coordination

Innovation

• Risk-sensitive safety filter using CBFs
• Centralized exponential risk operators for robustness
• Distributed strategies with agent-switching capability