• Journal of Drive and Control
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• v.20 no.4
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• pp.80-91
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• 2023

The purpose of this study is to suggest the direction for the development of intelligent agricultural machinery technology in the Republic of Korea. For this purpose, intelligent technology of agricultural machinery was divided into autonomous agricultural machinery and tractor-implement intelligent communication technology. Then, a survey and analysis of a previous study of the Republic of Korea and foreign countries were conducted. GNSS-based autonomous driving technology is still widely used worldwide, and recently, as research on camera and LiDAR-based autonomous driving is actively progressing, autonomous driving technology is becoming more advanced. ISOBUS-based technology is being developed worldwide for intelligent control of tractor-attached implements, and major global agricultural machinery manufacturers are actively applying it to their products. However, although some ISOBUS technologies are being researched in the Republic of Korea, there are no cases of application on agricultural machinery yet. Therefore, to be globally competitive in the agricultural machinery manufacturing industry, there is an urgent need to advance autonomous driving technology and commercialize agricultural machinery using ISOBUS technology.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• Korean Journal of Agricultural Science
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• v.45 no.1
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• pp.120-127
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• 2018

The rural labor force has gradually been decreasing due to the decrement of the farm population and the increment of the aging population. To solve these problems, it is necessary to develop and study autonomous agricultural machinery. Therefore, analyzing the dynamic behavior of vehicles in an autonomous agricultural environment is important. Until now, most studies on agricultural machinery, especially on ground vehicle dynamics, have been done by field tests. However, these field test methods are time consuming and costly with seasonal restrictions. A research method that can replace existing field test methods by using simulations is needed. In this study, we did basic research analyzing the effect of the travelling speed of a tractor on tire slip using simulation software. A tractor simulation model was developed based on field conditions following a straight path. The simulation was done for three ranges of speed: 20 - 30 km/h (considered the normal travelling speed range), 6 - 8 km/h (considered the plow tillage speed range) and 2 - 4 km/h (considered the rotary tillage speed range). The results of the simulation show that the slip ratio and slip angle values tended to increase as the traveling speed range of the tractor decreased. From the simulation results, it can be concluded that at low tractor speeds, it becomes more difficult to control the vehicle path. In future research, simulations will be done with various work environments such as a curved path as well as with various friction coefficient conditions, and the simulation results will be experimentally verified by applying them to an agricultural tractor.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.810-819
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• 1993

An autonomous lan vehicle for agriculture(ALVA-II) was developed. A prototype vehicle was made by modifying a commercial tractor. A Navigation sensor system with a geo-magnetic sensor performed the autonomous operations of ALVA-II, such as rotary tilling with headland turnings. A navigation sensor system with a machine vision system was also investigated to control ALVA-II following a work boudnary.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.551-557
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• 2014

This paper presents a robust lateral controller for autonomous guidance of a farm tractor in field operations. Although mechanical steering actuators have recently been used for passenger vehicles, the steering actuator of the farm tractor is based on a hydraulic system, resulting in limited bandwidth and a larger time delay. Based on a kinematic tractor model with steering actuator dynamics, a nonlinear control technique called dynamic surface control is applied to design a robust lateral controller that compensates for uncertainty owing to steering actuator and road geometry. Finally, tracking performance and robustness of the proposed controller are validated via commercial tractor simulations, with respect to the time delay of the steering actuator and road geometry (e.g., up and down hills), on a given field with a constant friction coefficient.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.704-712
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• 1999

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1997.06c
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• pp.41-48
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• 1997

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.02a
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• pp.554-559
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• 2000

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.12a
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• pp.511-517
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• 1998

• Journal of Drive and Control
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• v.17 no.1
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• pp.27-36
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• 2020

The aim of this study is to design a simulation model for an electric All-Wheel-Drive (AWD) tractor to evaluate the performance of the selected component and agricultural work ability. The electric AWD tractor consists of four motors independently for each drive wheel, and each motor is combined with an engine generator, a battery pack, and reducers. The torque data of a 78 kW-class tractor was measured during plow tillage and driving operation to develop a workload cycle. A simulation model was developed by using commercial software, Simulation X, and it used the workload as the simulation condition. As a result of simulation analysis, the drive system, including an electric motor and reducers, was able to cope with high load during plow tillage. The SOC (State of Charge) level was influenced by the output power of the motor, and it was maintained in the range of 50~80%. The fuel consumed by the engine was about 18.23 L during working on a total of 8 fields. The electric AWD tractor was able to perform agricultural work for about 7 hours. In the future study, the electric AWD tractor will be developed reflecting the simulation condition. Research on the comparison between the simulation model and the electric AWD tractor should be performed.