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Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning
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Title
Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning
DGIST Authors
Han, Joong-heePark, Chi-hoKwon, Jay HyounLee, JisunKim, Tae SooJang, Young Yoon
Issued Date
2020-07
Citation
Han, Joong-hee. (2020-07). Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning. doi: 10.3390/app10134667
Type
Article
Article Type
Article
Author Keywords
agricultural vehicleGNSS-RTKmotion sensorcrawler typeautonomous drivingsensor fusion
Keywords
Chemistry, MultidisciplinaryEngineering, MultidisciplinaryMaterials Science, MultidisciplinaryPhysics, Applied
ISSN
2076-3417
Abstract
The agriculture sector is currently facing the problems of aging and decreasing skilled labor, meaning that the future direction of agriculture will be a transition to automation and mechanization that can maximize efficiency and decrease costs. Moreover, interest in the development of autonomous agricultural vehicles is increasing due to advances in sensor technology and information and communication technology (ICT). Therefore, an autonomous driving control algorithm using a low-cost global navigation satellite system (GNSS)-real-time kinematic (RTK) module and a low-cost motion sensor module was developed to commercialize an autonomous driving system for a crawler-type agricultural vehicle. Moreover, an autonomous driving control algorithm, including the GNSS-RTK/motion sensor integration algorithm and the path-tracking control algorithm, was proposed. Then, the performance of the proposed algorithm was evaluated based on three trajectories. The Root Mean Square Errors (RMSEs) of the path-following of each trajectory are calculated to be 9, 7, and 7 cm, respectively, and the maximum error is smaller than 30 cm. Thus, it is expected that the proposed algorithm could be used to conduct autonomous driving with about a 10 cm-level of accuracy. © 2020 by the authors.
URI
http://hdl.handle.net/20.500.11750/12542
DOI
10.3390/app10134667
Publisher
MDPI AG
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