UAVによる能動分類のためのオンライン情報経路計画
Online Informative Path Planning for Active Classification Using UAVs
UAVが農地の雑草を効率的に検出するため、情報理論的目的に基づき3D空間で軌道をオンライン計画する手法を提案し、従来の全面走査より低エントロピーの地図を構築できることを示した。
著者: Marija Popovic, Gregory Hitz, Juan Nieto, Inkyu Sa, Roland Siegwart, Enric Galceran
分類: cs.RO
原文アブストラクト
In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate adaptive plans according to information-theoretic objectives, enabling the UAV to gather data efficiently. We validate our approach in simulation by comparing against existing methods, and study the effects of different planning strategies. Our results show that the proposed algorithm builds maps with over 50% lower entropy compared to traditional "lawnmower" coverage in the same amount of time. We demonstrate the planning scheme on a multirotor platform with different artificial farmland set-ups.
