Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.05011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914306651586560 |
|---|---|
| author | Gao, Xuting Pascual, Guillem Brown, Scott Martínez, Sonia |
| author_facet | Gao, Xuting Pascual, Guillem Brown, Scott Martínez, Sonia |
| contents | This paper studies the safe control of very large multi-agent systems via a generalized framework that employs so-called Banach Control Barrier Functions (B-CBFs). Modeling a large swarm as probability distribution over a spatial domain, we show how B-CBFs can be used to appropriately capture a variety of macroscopic constraints that can integrate with large-scale swarm objectives. Leveraging this framework, we define stable and filtered gradient flows for large swarms, paying special attention to optimal transport algorithms. Further, we show how to derive agent-level, microscopical algorithms that are consistent with macroscopic counterparts in the large-scale limit. We then identify conditions for which a group of agents can compute a distributed solution that only requires local information from other agents within a communication range. Finally, we showcase the theoretical results over swarm systems in the simulations section. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05011 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Banach Control Barrier Functions for Large-Scale Swarm Control Gao, Xuting Pascual, Guillem Brown, Scott Martínez, Sonia Optimization and Control Systems and Control 93Axx This paper studies the safe control of very large multi-agent systems via a generalized framework that employs so-called Banach Control Barrier Functions (B-CBFs). Modeling a large swarm as probability distribution over a spatial domain, we show how B-CBFs can be used to appropriately capture a variety of macroscopic constraints that can integrate with large-scale swarm objectives. Leveraging this framework, we define stable and filtered gradient flows for large swarms, paying special attention to optimal transport algorithms. Further, we show how to derive agent-level, microscopical algorithms that are consistent with macroscopic counterparts in the large-scale limit. We then identify conditions for which a group of agents can compute a distributed solution that only requires local information from other agents within a communication range. Finally, we showcase the theoretical results over swarm systems in the simulations section. |
| title | Banach Control Barrier Functions for Large-Scale Swarm Control |
| topic | Optimization and Control Systems and Control 93Axx |
| url | https://arxiv.org/abs/2602.05011 |