Learning safe control for multi-robot systems: Methods, verification, and open challenges
Authors:
- Kunal GargKunal Garg
- Songyuan ZhangSZSongyuan Zhang
- Oswin SoOSOswin So
- Charles DawsonCDCharles Dawson
- Chuchu FanCFChuchu Fan
- 21 March 2024
- journal article
- review article
- Published by Elsevier in Annual Reviews in Control
- Vol. 57
Abstract
No abstract availableKeywords
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Funding Information
- National Science Foundation
- Air Force Office of Scientific Research
This publication has 123 references indexed in Scilit:
- Safe multi-agent reinforcement learning for multi-robot controlArtificial Intelligence, 2023
- A review of cooperative multi-agent deep reinforcement learningApplied Intelligence, 2022
- Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms2021
- A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applicationsArtificial Intelligence Review, 2020
- A survey of distributed optimizationAnnual Reviews in Control, 2019
- Shield synthesisFormal Methods in System Design, 2017
- Some applications of polynomial optimization in operations research and real-time decision makingOptimization Letters, 2015
- Mean field gamesJapanese Journal of Mathematics, 2007
- Distributed attitude alignment in spacecraft formation flyingInternational Journal of Adaptive Control and Signal Processing, 2006
- On a characterization of flow‐invariant setsCommunications on Pure and Applied Mathematics, 1970