The proliferation of unmanned vehicles offers many opportunities for solving environmental sampling tasks with applications in resource monitoring and precision agriculture. Informative path planning (IPP) includes a family of methods which offer improvements over traditional surveying techniques for suggesting locations for observation collection. In this work, we present a novel solution to the IPP problem by using a coregionalized Gaussian processes to estimate a dynamic scalar field that varies in space and time. Our method improves previous approaches by using a composite kernel accounting for spatiotemporal correlations and at the same time, can be readily incorporated in existing IPP algorithms. Through extensive simulations, we show that our novel modeling approach leads to more accurate estimations when compared with formerly proposed methods that do not account for the temporal dimension.

翻译：无人载具的普及为解决环境采样任务提供了众多机遇，尤其在资源监测和精准农业领域具有重要应用。信息路径规划方法作为传统观测采集选址技术的改进方案已形成体系。本文提出一种基于协同区域化高斯过程的信息路径规划新方法，用于估计时空变化的动态标量场。该方法通过构建包含时空相关性的复合核函数改进了现有技术，同时可直接集成至现有信息路径规划算法中。大量仿真实验表明，与传统忽略时间维度的建模方法相比，本研究的创新建模方案能够实现更精确的估计效果。