  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.
