【第167期】GCBF+：安全的多智能体避障控制算法
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GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control
Summary
This research introduces a novel framework, GCBF+, for safe and scalable control of multi-agent systems using Graph Control Barrier Functions (GCBFs). The framework employs graph neural networks to learn GCBFs and distributed control policies, enabling agents to avoid collisions and reach goals using only local information. A key contribution is a theoretical result proving that a single GCBF can guarantee safety for multi-agent systems of…
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