Autonomous positioning of the unloading auger of a combine harvester by a laser sensor and GNSS

Cho, Wonjae; Kurita, Hiroki; Iida, Michihisa; Suguri, Masahiko; Masuda, R.

doi:10.1016/j.eaef.2015.01.004

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…These factors require real-time and accurate coordination to achieve optimal performance. Sensor and information communication technology integrated in intelligent harvesting robot could provide the best solution [100][101][102] . A computer program written in Visual Basic could be used to select optimum operating performance in grain crop thresher.…”

Section: Twin Rotor and Four Drum Of New Hollandmentioning

confidence: 99%

Review of grain threshing theory and technology

Chen

Han

et al. 2018

International Journal of Agricultural and Biological Engineering

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Threshing is the most important function of grain harvester. Grain loss and damage in harvesting are significantly related to threshing theory and technology. There are four kinds of threshing principles including impact, rubbing, combing and grinding. Four types of contact models between grain and threshing components have been constructed correspondently. Grain damage can be regarded as a function of peripheral velocity and contact pattern of impacting. Grain loss can be regarded as a function of contact pattern of rasp bars. Grain loss coming from cleaning and separation in the subsequent process of combing threshing was significantly decreased. Tangential and axial threshing technologies have been applied in grain threshing system widely. It showed that in the combined application, tangential rolls are used to accelerate grain flow, and axial rolls are used to increase threshing quality especially lower loss and damage. Conical concave may take the place of the traditional cylindrical one. With the development of sensor technology and communication technology, intelligent harvesting robot and automatic threshing system will be integrated together to improve grain quality and operation comfort.

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Section: Twin Rotor and Four Drum Of New Hollandmentioning

confidence: 99%

Review of grain threshing theory and technology

Chen

Han

et al. 2018

International Journal of Agricultural and Biological Engineering

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“…The experimental results are stable for 6 tests in the field. Reference 16 provided an another container positioning method based on the laser sensor and GNSS [ 16 ], the positioning errors are: maximum error of (161 mm, 116 mm, 65 mm), mean error of (68 mm, 60 mm, 26 mm), and RMSE of (83 mm, 67 mm, 32 mm) in x, y, and z axes, respectively. Compared with Table 2 , the method proposed in this paper has better positioning accuracy.…”

Section: Field Experiments and Results Analysismentioning

confidence: 99%

“…Therefore, the position of the container can be indirectly calculated by identifying the position and posture of the marker. Cho et al [ 16 ] proposed a method for the autonomous positioning of the unloading auger and the grain container by a laser sensor and global navigation satellite system (GNSS). The method was sufficient for the successful positioning of the auger spout within the acceptable error range.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting

Zhang

Gong

Chen

et al. 2021

Sensors

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In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

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“…A conventional and an X-turn path planning method were provided for an automated guidance system for tracked combine harvesters [5]. The development of an autonomous navigation system using a laser sensor and global navigation satellite system (GNSS) [6], a laser scanner [7], and a Differential Global Positioning System (DGPS) [8] have been studied. However, the development of an automatic tractor navigation system is still developing to get precise track information and an optimal method.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control of an Autonomous Head Feeding Combine

Sutisna¹,

Setiawan²,

Subrata³

et al. 2021

I2M

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This study aims to develop an autonomous combine harvester. A manual steering combine harvester was modified autonomously using navigation systems of an RTK-DGPS, a gyroscope, and crawler speed sensors. These sensors could determine the combine position and heading required to guide the path. The control system is processed for these navigation sensors' data to make the decision of combine movement. Moreover, it commands the actuator to move the steering lever mechanism. The steering control's desired heading angle was determined from lateral error, heading error, and the traveling speed. In this study, the combined harvester's average forward traveling speed was set at 0.17 m/s, adjusted to a navigation sensor's sampling rate of 5 Hz and the steering mechanism delay. The preliminary test showed the combine could turn by pivoting one of its tracks which turned the radius was into 0.4 m. Furthermore, a guidance control system of the combine harvester was developed based on this test result. The developed guidance control system was successfully guiding the combine to follow the harvesting path. The test results showed that the root mean square of the lateral error was less than 0.1 m.

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Autonomous positioning of the unloading auger of a combine harvester by a laser sensor and GNSS

Cited by 10 publications

References 9 publications

Review of grain threshing theory and technology

Review of grain threshing theory and technology

Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting

Tracking Control of an Autonomous Head Feeding Combine

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