Learning safe control for multi-robot systems: Methods, verification, and open challenges-Reference-Cited by-同舟云学术

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Learning safe control for multi-robot systems: Methods, verification, and open challenges

Published:2024 Issue: Volume:57 Page:100948
ISSN:1367-5788
Container-title:Annual Reviews in Control
language:en
Short-container-title:Annual Reviews in Control

Author:

Garg Kunal^ORCID,Zhang Songyuan,So Oswin,Dawson Charles,Fan Chuchu

Funder

National Science Foundation

Air Force Office of Scientific Research

Publisher

Elsevier BV

Reference237 articles.

1. FOSSIL: A software tool for the formal synthesis of Lyapunov functions and barrier certificates using neural networks;Abate,2021

2. Constrained policy optimization;Achiam,2017

3. Multi-agent trajectory tracking with self-triggered cloud access;Adaldo,2016

4. Safe control synthesis via input constrained control barrier functions;Agrawal,2021

5. Some applications of polynomial optimization in operations research and real-time decision making;Ahmadi;Optimization Letters,2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. How to Train Your Neural Control Barrier Function: Learning Safety Filters for Complex Input-Constrained Systems;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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