用图引导与分段优化，高效降低复杂环境中的感知不确定性。
Hierarchical Informative Path Planning via Graph Guidance and Trajectory Optimization
- 分三阶段：先图规划全局路径，再按几何和核函数边界分配预算，最后用样条优化每段。
- 在合成环境和北极数据集上，后验不确定度低于图法和连续优化基线。
- 比梯度法快9倍、黑箱优化快20倍，适合障碍密集场景的实时感知任务。
研究在障碍物密集环境中，基于旅行预算的知情路径规划（IPP），即通过采集高斯过程（GP）建模的隐式场数据，以减少目标位置的不确定性。图法求解器虽具全局最优性，但需预设测量点；连续轨迹优化支持路径感知，却计算量大且对初始化敏感。本文提出分层框架：(i) 图基全局规划，(ii) 利用几何与核函数界进行分段预算分配，(iii) 基于样条的每段优化，含硬约束与障碍物剔除。结合全局引导与局部精修，该方法在合成障碍环境与北极数据集上，均实现低于图法与连续基线的后验不确定性，同时运行速度比梯度法快至9倍、黑箱优化快至20倍。
原文摘要 · Abstract (English)
We study informative path planning (IPP) with travel budgets in cluttered environments, where an agent collects measurements of a latent field modeled as a Gaussian process (GP) to reduce uncertainty at target locations. Graph-based solvers provide global guarantees but assume pre-selected measurement locations, while continuous trajectory optimization supports path-based sensing but is computationally intensive and sensitive to initialization in obstacle-dense settings. We propose a hierarchical framework with three stages: (i) graph-based global planning, (ii) segment-wise budget allocation using geometric and kernel bounds, and (iii) spline-based refinement of each segment with hard constraints and obstacle pruning. By combining global guidance with local refinement, our method achieves lower posterior uncertainty than graph-only and continuous baselines, while running faster than continuous-space solvers (up to 9x faster than gradient-based methods and 20x faster than black-box optimizers) across synthetic cluttered environments and Arctic datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。