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.

翻译：本研究针对杂乱环境中的信息路径规划问题，在旅行预算约束下，通过智能体采集由高斯过程建模的隐变量场测量值，以降低目标位置的不确定性。基于图的求解器虽能提供全局保证，但需预设测量位置；而连续轨迹优化方法虽支持基于路径的感知，但在障碍密集场景中计算量大且对初始化敏感。我们提出一个包含三个阶段的分层框架：（i）基于图的全局规划，（ii）利用几何与核函数边界进行分段预算分配，以及（iii）在硬约束与障碍剪枝条件下对各分段进行基于样条的轨迹细化。通过融合全局引导与局部优化，本方法在合成杂乱环境与北极数据集上的实验表明：相较于纯图方法与连续基线方法，本方法能获得更低的后验不确定性，同时运行速度显著快于连续空间求解器（较基于梯度的方法快达9倍，较黑盒优化器快达20倍）。