Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.

中文翻译：

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用于基于无人机的地形监视的信息路径规划框架

无人机在广泛的监视和研究应用中代表了一个新领域。为了充分利用其潜力，一项关键挑战是计划任务以在复杂环境中高效地获取数据。为了解决此问题，本文介绍了一种通用的信息路径规划框架，该框架用于使用航空机器人监视场景，重点关注传感器信息的值在目标区域中分布不均且先验未知的问题。该方法通过使用从概率传感器接收的可变分辨率数据进行自适应，以在地形上映射离散变量或连续变量，从而能够学习并关注感兴趣的区域。在执行任务期间 通过优化通过粗网格搜索获得的初始解，在线构建的地形图可用于计划连续3-D空间中信息丰富的轨迹。大量的仿真表明，我们的方法比现有方法更有效。我们还使用公开可用的数据集和农业监控任务的概念证明，演示了其在真实感地图上的实时应用。