空陸協調ロボティクスによる精密農業システムの構築
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
小型UAVの空撮とUGVの選択的作業を組み合わせ、作物モニタリングから除草・可変散布までを人手なしで行う適応型精密農業ロボットシステムを開発・実証した。
著者: Alberto Pretto, Stéphanie Aravecchia, Wolfram Burgard, Nived Chebrolu, Christian Dornhege, Tillmann Falck, Freya Fleckenstein, Alessandra Fontenla, Marco Imperoli, Raghav Khanna, Frank Liebisch, Philipp Lottes, Andres Milioto, Daniele Nardi, Sandro Nardi, Johannes Pfeifer, Marija Popović, Ciro Potena, Cédric Pradalier, Elisa Rothacker-Feder, Inkyu Sa, Alexander Schaefer, Roland Siegwart, Cyrill Stachniss, Achim Walter, Wera Winterhalter, Xiaolong Wu, Juan Nieto
分類: cs.RO
原文アブストラクト
The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention.
