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English(EN) 3D Underwater Path Planning via Generative Flow Field Surrogates

AI生成流场以实现更快的3D水下导航

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的3D水下路径规划方法，通过使用生成流场代理，特别是条件生成对抗网络（cGAN）。这些cGAN，包括PatchGAN和具有自注意力机制的2D3DGAN，可以替代计算成本高昂的雷诺平均纳维-斯托克斯（RANS）模拟。生成模型能够快速合成复杂的流场体积，从而实现自主水下航行器（AUVs）的实时路径规划。该方法显著降低了能源消耗，并避免了高速尾流的遭遇，为海上机器人提供了实用的解决方案。 AI

影响 通过用快速生成模型替代缓慢的模拟，实现水下航行器的实时路径规划。

排序理由 该集群包含一篇详细介绍使用AI进行路径规划的新方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI生成流场以实现更快的3D水下导航

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该集群包含一篇详细介绍使用AI进行路径规划的新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth, Karl Sammut ·

    通过生成流场代理进行三维水下路径规划

    arXiv:2606.06077v1 Announce Type: cross Abstract: Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a …

  2. arXiv cs.LG TIER_1 English(EN) · Karl Sammut ·

    通过生成流场代理进行三维水下路径规划

    Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a uniform current model. High-fidelity Reynolds-Aver…

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