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Development of Path Generation and Algorithm for Autonomous Combine Harvester Using Dual GPS Antenna.
Lee, Kyuho; Choi, Hyohyuk; Kim, Junghun.
Afiliação
  • Lee K; Autonomous Vehicle Intelligent Robotics, Industrial Convergence Interdepartmental Program, Hongik University, Seoul 04066, Republic of Korea.
  • Choi H; Autonomous Vehicle Intelligent Robotics, Industrial Convergence Interdepartmental Program, Hongik University, Seoul 04066, Republic of Korea.
  • Kim J; Autonomous Vehicle Intelligent Robotics, Industrial Convergence Interdepartmental Program, Hongik University, Seoul 04066, Republic of Korea.
Sensors (Basel) ; 23(10)2023 May 21.
Article em En | MEDLINE | ID: mdl-37430857
ABSTRACT
Research on autonomous driving technology is actively underway to solve the facing problems in the agricultural field. Combine harvesters used in East Asian countries, including Korea, are tracked-type vehicles. The steering control system of the tracked vehicle has different characteristics from the wheeled vehicle used in the agricultural tractor. In this paper, a dual GPS antenna-based autonomous driving system and path tracking algorithm were developed for a robot combine harvester. An α-turn-type work path generation algorithm and a path tracking algorithm were developed. The developed system and algorithm were verified through experiments using actual combine harvesters. The experiment consisted of an experiment with harvesting work and an experiment without harvesting work. In the experiment without harvesting work, an error of 0.052 m occurred during working driving and 0.207 m during turning driving. In the experiment where the harvesting work was carried out, an error of 0.038 m occurred during work driving and 0.195 m during turning driving. As a result of comparing the non-work area and driving time to the results of manual driving, the self-driving experiment with harvesting work showed an efficiency of 76.7%.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article