A collaborative operation system of autonomous unmanned aerial vehicles for field phenotyping in farm fields

Hyeon-Seung Lee; Beom-Soo Shin; J. Alex Thomasson; Xiongzhe Han

doi:10.1117/12.2585866

12 April 2021 A collaborative operation system of autonomous unmanned aerial vehicles for field phenotyping in farm fields

Hyeon-Seung Lee, Beom-Soo Shin, J. Alex Thomasson, Xiongzhe Han

Author Affiliations +

Proceedings Volume 11747, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI; 117470E (2021) https://doi.org/10.1117/12.2585866
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have significant potential in agriculture and smart farming applications for the efficient monitoring of plant growth, the irrigation process, disease detection, etc. Most research on field phenotyping with remote sensing was accomplished by a typical UAV equipped with an RGB camera or a multispectral camera over a large farm field. Due to the effects of wind disturbances on point-cloud generation processing with a single-camera image captured from the UAV, precise field phenotyping measurement for crop breeding and agriculture production requires the simultaneous collection of images by multiple cameras that are far enough apart to provide for structure from motion calculations. To improve digital surface models by minimizing measurement errors caused by the motion of the UAV and plants during a flying mission, a cooperative operation system of multiple UAVs was proposed to enable the simultaneous collection of images from different perspectives. A coordinated navigation system based on the Robot Operation System was constructed to compute control commands to stabilize pose control and the location of the UAVs. Based on a leader-follower formation control algorithm through a wireless network system, a follower UAV performed coordination with a leader UAV to maintain the desired constant speed, direction, and percentage of image overlap in a synchronized motion, ultimately enabling task achievement in a short time and improvement of target models based on 3D reconstruction. To validate the performance of the proposed method, measurement errors of field phenotyping, obtained from synchronized multiple UAV-based image collection, were compared with the single UAV-based image collection in simulation and field tests.

Conference Presentation

Citation Download Citation

Hyeon-Seung Lee, Beom-Soo Shin, J. Alex Thomasson, and Xiongzhe Han "A collaborative operation system of autonomous unmanned aerial vehicles for field phenotyping in farm fields", Proc. SPIE 11747, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI, 117470E (12 April 2021); https://doi.org/10.1117/12.2585866

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