In this paper, we introduce an informative path planning (IPP) framework for
active classification using unmanned aerial vehicles (UAVs). Our algorithm uses
a combination of global viewpoint selection and evolutionary optimization to
refine the planned trajectory in continuous 3D space while satisfying dynamic
constraints. Our approach is evaluated on the application of weed detection for
precision agriculture. We model the presence of weeds on farmland using an
occupancy grid and generate adaptive plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We validate our
approach in simulation by comparing against existing methods, and study the
effects of different planning strategies. Our results show that the proposed
algorithm builds maps with over 50% lower entropy compared to traditional
"lawnmower" coverage in the same amount of time. We demonstrate the planning
scheme on a multirotor platform with different artificial farmland set-ups.

本文介绍了一种利用无人机进行主动分类的信息路径规划框架，应用于精准农业的杂草检测。它利用全局视点选择和进化优化相结合，在连续的三维空间中优化计划轨迹，同时考虑动态约束；基于占用格子模型对农田中杂草的存在进行建模，并根据信息理论目标生成自适应计划，使无人机高效地收集数据。仿真结果表明，与传统的均匀覆盖方法相比，在同样的时间内建立了熵较低的地图。