Robust mission planning of UAV networks for environmental sensing

Abstract

In this paper, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this paper, we leverage the effects of weather conditions on pollution measurements' quality in order to design a UAV mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a VOC pollution dataset and show a high performance improvement that is due to the fine characterization of the measurement errors.