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新的PS-MAPF算法解决了自动化系统中复杂的多智能体路径寻找问题

研究人员推出了一种新的多智能体路径寻找(Multi-Agent Path Finding)变体——Pivot-and-Station Multi-Agent Path Finding (PS-MAPF),该变体专为自动化高密度存储系统设计。该变体要求一部分智能体在所有智能体到达匿名站点之前访问可互换的枢轴点。该研究全面刻画了PS-MAPF实例的可解性,表明在2边连通图上的所有实例都是可解的,并给出了在任意连通图上的可解性条件。此外,研究证明了即使只有一个枢轴点,最小化站点完成时间或站点流动时间也是NP难的,并提出了三种算法,包括Pivot-Prioritized Planning (PPP),该算法在解决基准实例方面显著优于基线算法。 AI

影响 为优化自动化系统中复杂的多智能体协调引入了一个新的算法框架。

排序理由 这是一篇详细介绍AI子领域新算法和理论发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的PS-MAPF算法解决了自动化系统中复杂的多智能体路径寻找问题

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这是一篇详细介绍AI子领域新算法和理论发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andrea Di Nezza, Mihir Patel, Fabio Fagnani, Sara Bernardini ·

    Pivot-and-Station 多智能体路径寻找:可解性、复杂性与算法

    arXiv:2608.24585v1 Announce Type: new Abstract: Automated high-density storage systems (warehouses, robotic parking, plant logistics, etc.) require fleets of agents to move through scarce task-critical resources and then park without obstructing future operations. We introduce Pi…