English(EN) Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing

AI研究解决GPS欺骗无人机分离问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一种新方法，即使在GPS信号降级或被欺骗的情况下，也能确保小型无人机系统（sUAS）之间的分离。该方法利用多智能体强化学习（MARL）为智能体创建了一个鲁棒的对抗策略，将损坏的位置广播视为与对手的零和博弈。该方法推导出了对抗性扰动的闭式表达式，从而能够进行高效计算，并在GPS严重损坏的模拟中展示了接近零的碰撞率。 AI

影响这项研究可以提高在GPS信号受损环境中无人机运行的安全性和可靠性。

排序理由这是一篇发表在arXiv上的研究论文，详细介绍了一种新颖的多智能体强化学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei · 2026-08-25 04:00

GPS降级和欺骗下的无人机小型分离保障的鲁棒多智能体强化学习

arXiv:2603.28900v2 Announce Type: replace-cross Abstract: We address robust separation assurance for small Unmanned Aircraft Systems (sUAS) under GPS degradation and spoofing via Multi-Agent Reinforcement Learning (MARL). In cooperative surveillance, each aircraft (or agent) broa…

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GPS降级和欺骗下的无人机小型分离保障的鲁棒多智能体强化学习

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