2019/09/18 by Yoonchang Sung, Sung, Yoonchang, Deeksha Dixit +3
Computer Science · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #Gaussian Processes and Bayesian Inference #Robotics (cs.RO) #Signal Processing (eess.SP) #Target Tracking and Data Fusion in Sensor Networks #cs.RO #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1909.08483
9 pages, 7 figures, Published in IEEE International Conference on Robotics and Automation (ICRA), 2021
openalex publication_date 2019/09/18 · arxiv created 2021/03/22 · arxiv updated 2021/03/24 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Our work is motivated by environmental monitoring tasks, where finding the global maxima (i.e., hotspot) of a spatially varying field is crucial. We investigate the problem of identifying the hotspot for fields that can be sensed using an Unmanned Aerial Vehicle (UAV) equipped with a downward-facing camera. The UAV has a limited time budget which it can use for learning the unknown field and identifying the hotspot. Our contribution is to show how this problem can be formulated as a novel multi-fidelity variant of the Gaussian Process (GP) multi-armed bandit problem. The novelty is two-fold: (i) unlike standard multi-armed bandit settings, the rewards of the arms are correlated with each other; and (ii) unlike standard GP regression, the measurements in our problem are images (i.e., vector measurements) whose quality depends on the altitude of the UAV. We present a strategy for finding the sequence of UAV sensing locations and empirically compare it with several baselines. Experimental results using images gathered onboard a UAV are also presented and the scalability of the proposed methodology is assessed in a large-scale simulated environment in Gazebo.