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Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

Authors :
Pretto, Alberto
Aravecchia, Stéphanie
Burgard, Wolfram
Chebrolu, Nived
Dornhege, Christian
Falck, Tillmann
Fleckenstein, Freya
Fontenla, Alessandra
Imperoli, Marco
Khanna, Raghav
Liebisch, Frank
Lottes, Philipp
Milioto, Andres
Nardi, Daniele
Nardi, Sandro
Pfeifer, Johannes
Popović, Marija
Potena, Ciro
Pradalier, Cédric
Rothacker-Feder, Elisa
Sa, Inkyu
Schaefer, Alexander
Siegwart, Roland
Stachniss, Cyrill
Walter, Achim
Winterhalter, Wera
Wu, Xiaolong
Nieto, Juan
Source :
IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29-49, Sept. 2021
Publication Year :
2019

Abstract

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.<br />Comment: Published in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29-49, Sept. 2021

Subjects

Subjects :
Computer Science - Robotics

Details

Database :
arXiv
Journal :
IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29-49, Sept. 2021
Publication Type :
Report
Accession number :
edsarx.1911.03098
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/MRA.2020.3012492