限られた時間でUAVが環境ホットスポットを特定するマルチフィデリティGPバンディット手法
Environmental Hotspot Identification in Limited Time with a UAV Equipped with a Downward-Facing Camera
下向きカメラ搭載UAVが限られた時間予算で未知の空間場の最大値(ホットスポット)を効率的に探索するため、画像計測の高度依存品質を考慮した新規マルチフィデリティ型ガウス過程マルチアームドバンディット問題として定式化し、UAVの観測位置列を決定する戦略を提案・評価した論文。
著者: Yoonchang Sung, Deeksha Dixit, Pratap Tokekar
分類: cs.RO, eess.SP
原文アブストラクト
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.
