In missions constrained by finite resources, efficient data collection is critical. Informative path planning, driven by automated decision-making, optimizes exploration by reducing the costs associated with accurate characterization of a target in an environment. Previous implementations of active learning did not consider the action cost for regression problems or only considered the action cost for classification problems. This paper analyzes an AL algorithm for Gaussian Process regression while incorporating action cost. The algorithm's performance is compared on various regression problems to include terrain mapping on diverse simulated surfaces along metrics of root mean square error, samples and distance until convergence, and model variance upon convergence. The cost-dependent acquisition policy doesn't organically optimize information gain over distance. Instead, the traditional uncertainty metric with a distance constraint best minimizes root-mean-square error over trajectory distance. This studys impact is to provide insight into incorporating action cost with AL methods to optimize exploration under realistic mission constraints.

翻译：在资源有限的探测任务中，高效数据采集至关重要。基于自动化决策的信息化路径规划，通过降低环境中目标精确表征的相关成本，优化了探索过程。以往的主动学习实现未考虑回归问题的行动成本，或仅考虑了分类问题的行动成本。本文分析了结合行动成本的高斯过程回归主动学习算法。该算法在多种回归问题上进行了性能比较，包括在不同模拟表面进行地形测绘，评估指标包括收敛前的均方根误差、采样次数与距离，以及收敛时的模型方差。成本依赖的采集策略并未自然优化信息增益与距离的关系。相反，采用距离约束的传统不确定性指标能最佳地最小化轨迹距离上的均方根误差。本研究的意义在于为在实际任务约束下结合行动成本与主动学习方法以优化探索提供了见解。