Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914206062739456 |
|---|---|
| author | Lederer, Armin Noorani, Erfaun Krause, Andreas |
| author_facet | Lederer, Armin Noorani, Erfaun Krause, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems Lederer, Armin Noorani, Erfaun Krause, Andreas Systems and Control Artificial Intelligence Machine Learning 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. |
| title | Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems |
| topic | Systems and Control Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.07347 |