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Division of AI, Big data and Block chain
1. Journal Articles
Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment
Han, Joong-hee
;
Park, Chi-ho
;
Jang, Young Yoon
;
Gu, Ja Duck
;
Kim, Chan Young
Division of AI, Big data and Block chain
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Title
Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment
DGIST Authors
Han, Joong-hee
;
Park, Chi-ho
;
Jang, Young Yoon
;
Gu, Ja Duck
;
Kim, Chan Young
Issued Date
2022-01
Citation
Han, Joong-hee. (2022-01). Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment. doi: 10.3390/s22010114
Type
Article
Author Keywords
autonomous driving
;
agricultural vehicle
;
sensor fusion
;
GNSS
;
motion sensor
ISSN
1424-8220
Abstract
To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A prototype of the agricultural vehicle was created using a rubber-tracked vehicle equipped with two AC motors. A prototype of the autonomous driving hardware consisted of a GNSS module, a motion sensor, an embedded board, and an LTE module, and it was made for less than $1000. Additional software, including a sensor fusion algorithm for positioning and a path-tracking algorithm for autonomous driving, were implemented. Then, the performance of the autonomous driving agricultural vehicle was evaluated based on two trajectories in an apple farm. The results of the field test determined the RMS, and the maximums of the path-following errors were 0.10 m, 0.34 m, respectively. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/16032
DOI
10.3390/s22010114
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
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Park, Chi-Ho
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