Citation: Lee, H.-S.; Shin, B.-S.;
Thomasson, J.A.; Wang, T.; Zhang, Z.;
Han, X. Development of Multiple
UAV Collaborative Driving Systems
for Improving Field Phenotyping.
Sensors 2022, 22, 1423. https://
doi.org/10.3390/s22041423
Academic Editors: Sindhuja
Sankaran and Biswajeet Pradhan
Received: 27 December 2021
Accepted: 10 February 2022
Published: 12 February 2022
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Article
Development of Multiple UAV Collaborative Driving Systems
for Improving Field Phenotyping
Hyeon-Seung Lee
1,2
, Beom-Soo Shin
1,2
, J. Alex Thomasson
3
, Tianyi Wang
4
, Zhao Zhang
5,6
and Xiongzhe Han
1,2,
*
1
Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National
University, Chuncheon 24341, Korea; hslee91@kangwon.ac.kr (H.-S.L.); bshin@kangwon.ac.kr (B.-S.S.)
2
Interdisciplinary Program in Smart Agriculture, College of Agriculture and Life Sciences,
Kangwon National University, Chuncheon 24341, Korea
3
Department of Agricultural and Biological Engineering, Mississippi State University,
Starkville, MS 39762, USA; athomasson@abe.msstate.edu
4
College of Engineering, China Agricultural University, Beijing 100083, China; timothywangty@tamu.edu
5
Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural
University, Beijing 100083, China; zhaozhangcau@cau.edu.cn
6
Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture and Rural Affairs
of China, China Agricultural University, Beijing 100083, China
* Correspondence: hanxiongzhe@kangwon.ac.kr; Tel.: +82-33-250-6473
Abstract:
Unmanned aerial vehicle-based remote sensing technology has recently been widely
applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these
technologies have considerable potential in smart agriculture applications. Field phenotyping using
remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras
or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken
from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors
may occur due to the movement of the drone and plants during flight. In this study, to minimize
measurement error and improve the digital surface model, we proposed a collaborative driving
system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An
integrated navigation system based on MAVSDK is configured for the attitude control and position
control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm
and a long-range wireless network system, the follower drone cooperates with the leader drone to
maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their
phenotyping. A collision avoidance algorithm was developed because different UAVs can collide
due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and
optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based
simulation environment was established. Based on the algorithm that has been verified and optimized
in the previous simulation environment, some unmanned aerial vehicles were flown in the same
flight path in a real field, and the simulation and the real field were compared. As a result of the
comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight
accuracy was 0.46 m, showing flight accuracy like that of a commercial program.
Keywords:
multiple UAVs; remote sensing; collaborative driving; field phenotyping; synchro-
nized motion
1. Introduction
Owing to the recent breakthrough in unmanned aerial vehicles (UAVs) or drones,
their applications in the agricultural field, such as in crop monitoring, detection of crop
diseases, digital surface modeling (DSM), sowing, spraying, irrigation, and mapping,
have significantly reduced working hours and labor requirements, greatly improving the
Sensors 2022, 22, 1423. https://doi.org/10.3390/s22041423 https://www.mdpi.com/journal/sensors
