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English(EN) Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems

新型神经Q路由优化工业机器人车队

研究人员开发了一种名为神经双Q路由的新机器学习方法,用于优化大规模工业机器人车队,特别是在半导体制造工厂常见的顶装式起重运输(OHT)系统中。该方法通过使用共享神经网络来估计状态-动作值,改进了传统的Q路由,从而实现了不同路由上下文之间的更好信息共享。该系统通过模拟轨迹进行初始化,并在在线进行精炼,在各种车队规模下,与表格双Q路由相比,平均完成时间减少了高达8.8%,并在启动场景中减少了尾部完成时间。 AI

影响 这种新的路由方法可以显著提高自动化工业物流系统的效率并减少等待时间。

排序理由 该集群包含一篇详细介绍新机器学习算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新型神经Q路由优化工业机器人车队

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该集群包含一篇详细介绍新机器学习算法的研究论文。[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) · Cheng Gu, Qiusheng Zhao, Anbang Liu, Shaochong Lin, Max Z. J. Shen ·

    面向大规模架空起重运输系统的轨迹初始化神经双Q路由

    arXiv:2608.30512v1 Announce Type: cross Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersection access, downstream blocking, and station contention. We study this problem in …