发表机构
University of Sevilla（塞维利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对水生环境监测中的信息路径规划，提出用深度集成替代高斯过程以提供校准的不确定性，实验表明该方法将重建误差降低83%，且蒙特卡洛树搜索在低计算成本下达到最优规划性能。
AI 中文摘要
标量场重建的信息路径规划利用预测不确定性来引导传感载具前往信息量最大的位置。高斯过程提供了这一信号，但其平稳各向同性核函数对于诸如石油泄漏之类的非均匀现象而言设定错误，导致产生校准不当的估计，从而降低规划性能。我们研究用校准良好的深度集成替代高斯过程是否能改善路径规划结果，以及不确定性质量是否与规划算法的选择相互作用。五种策略（ε-贪心、价值贪心、不确定性贪心、蒙特卡洛树搜索和滚动时域定向运动）共享一个基于物理的石油泄漏模拟训练的深度集成主干。在留出的随机泄漏场景中，深度集成相对于高斯过程基线将归一化重建误差降低了83%。至关重要的是，校准良好的不确定性放大了规划策略的重要性：在校准不当的模型下，算法之间的性能差距可忽略不计，但在集成下差距变得显著，其中多步前瞻规划器在重建误差上比贪心选择最多改善32%，并实现IoU超过0.85。蒙特卡洛树搜索是推荐的规划器，在重建质量上与定向运动相匹配，而计算成本低一个数量级。
英文摘要
Informative Path Planning for scalar field reconstruction uses predictive uncertainty to direct sensing vehicles toward maximally informative locations. Gaussian Processes provide this signal but their stationary isotropic kernels are misspecified for non-homogeneous phenomena such as oil spills, producing miscalibrated estimates that degrade planning. We investigate whether replacing the Gaussian Process with a well-calibrated Deep Ensemble improves path planning outcomes, and whether uncertainty quality interacts with the choice of planning algorithm. Five strategies ($ε$-Greedy, Value Greedy, Uncertainty Greedy, Monte Carlo Tree Search, and Receding Horizon Orienteering) share a common Deep Ensemble backbone trained on physics-based oil spill simulations. On held-out stochastic spill scenarios, the Deep Ensemble reduces normalised reconstruction error by $83\%$ relative to the Gaussian Process baseline. Crucially, well-calibrated uncertainty amplifies the importance of the planning strategy: the performance gap between algorithms is negligible under miscalibrated models but becomes substantial under the ensemble, where multi-step lookahead planners outperform greedy selection by up to $32\%$ in reconstruction error and achieve IoU above $0.85$. Monte Carlo Tree Search is the recommended planner, matching Orienteering in reconstruction quality at an order-of-magnitude lower computational cost.
