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Learning safe control for multi-robot systems: Methods, verification, and open challenges
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DOI：10.1016/j.arcontrol.2024.100948.png)
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Abstract
En 中文
In this survey, we review the recent advances in control design methods for robotic multi-agent systems (MAS), focusing on learning-based methods with safety considerations. We start by reviewing various notions of safety and liveness properties, and modeling frameworks used for problem formulation of MAS. Then we provide a comprehensive review of learning-based methods for safe control design for multi -robot systems. We start with various shielding-based methods, such as safety certificates, predictive filters, and reachability tools. Then, we review the current state of control barrier certificate learning in both a centralized and distributed manner, followed by a comprehensive review of multi-agent reinforcement learning with a particular focus on safety. Next, we discuss the state -of -the -art verification tools for the correctness of learning-based methods. Based on the capabilities and the limitations of the state -of -the -art methods in learning and verification for MAS, we identify various broad themes for open challenges: how to design methods that can achieve good performance along with safety guarantees; how to decompose single-agent-based centralized methods for MAS; how to account for communication-related practical issues; and how to assess transfer of theoretical guarantees to practice.
Keywords:
Safe multi-agent reinforcement learning
Certificate-based multi-agent control
Verification for multi-agent systems
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