Uncrewed Aerial Vehicles (UAVs) are a leading choice of platforms for a variety of information-gathering applications. Sensor planning can enhance the efficiency and success of these types of missions when coupled with a higher-level informative path-planning algorithm. This paper aims to address these data acquisition challenges by developing an informative non-myopic sensor planning framework for a single-axis gimbal coupled with an informative path planner to maximize information gain over a prior information map. This is done by finding reduced sensor sweep bounds over a planning horizon such that regions of higher confidence are prioritized. This novel sensor planning framework is evaluated against a predefined sensor sweep and no sensor planning baselines as well as validated in two simulation environments. In our results, we observe an improvement in the performance by 21.88% and 13.34% for the no sensor planning and predefined sensor sweep baselines respectively.

翻译：无人机已成为各类信息采集应用中的首选平台。当与高层级信息感知路径规划算法结合时，传感器规划能够提升此类任务的效率与成功率。本文旨在通过开发一种面向单轴云台的非短视信息感知规划框架，并结合信息感知路径规划器，以在先验信息地图上最大化信息增益，从而应对数据采集的挑战。该框架通过规划时域内寻找缩减的传感器扫描边界，实现对高置信度区域的优先覆盖。这一新型传感器规划框架通过预定义传感器扫描和无传感器规划基线进行对比评估，并在两种仿真环境中得到验证。实验结果表明，相较于无传感器规划基线和预定义传感器扫描基线，本框架分别实现了21.88%和13.34%的性能提升。