Unmanned Aerial Vehicles (UAVs) 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 (IPP) 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
demonstrate a proof of concept for an agricultural monitoring task.

该论文介绍了一种基于无人机的信息路径规划框架，在复杂环境下实现高效数据采集，通过对地形进行离散或连续变量的映射，学习和关注感兴趣区域，并通过优化初值解得到连续 3D 空间中信息丰富的轨迹，证明该方法比现有方法更高效。