用分层贝叶斯优化提升深海测绘路径规划效率，兼顾实时性与全局策略。
Efficient Non-Myopic Layered Bayesian Optimization For Large-Scale Bathymetric Informative Path Planning
- 分两层设计贝叶斯优化，实现非短视的实时路径规划
- 在真实海底地形上比传统路线和贪心方法提升23%以上映射效率
- 适用于嵌入式平台的无人潜水器深海测绘任务
基于贝叶斯优化的测深信息路径规划（IPP）可使无人潜航器（AUV）聚焦于特征丰富区域，快速降低不确定性并提高测绘效率。现有基于高斯过程（GP）的地图贝叶斯优化方法在小范围场景中表现良好，但在大范围测绘时存在短视且计算量大的问题，难以实际部署。为此，本文提出一种两层式贝叶斯优化路径规划方法，在大规模随机变分高斯过程（Stochastic Variational GP）地图上以树搜索方式实现非短视、实时的路径规划，同时满足潜航器运动约束并考虑定位不确定性。在嵌入式平台上的硬件在环（HIL）实验中，该框架优于标准工业级割草式路径和贪心基线，在真实测深数据上表现出显著更高的映射效率。
原文摘要 · Abstract (English)
Informative path planning (IPP) applied to bathymetric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are short-sighted and computationally heavy when mapping larger areas, hindering deployment in real applications. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, real-time planning in a tree search fashion over large Stochastic Variational GP maps, while respecting the AUV motion constraints and accounting for localization uncertainty. Our framework outperforms the standard industrial lawn-mowing pattern and a myopic baseline in a set of hardware in the loop (HIL) experiments in an embedded platform over real bathymetry.
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