Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting
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 pro...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1166 |
| _version_ | 1797413308153724928 |
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| author | Wei Zhang Liang Gong Suyue Chen Wenjie Wang Zhonghua Miao Chengliang Liu |
| author_facet | Wei Zhang Liang Gong Suyue Chen Wenjie Wang Zhonghua Miao Chengliang Liu |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:15:47Z |
| format | Article |
| id | doaj.art-38a7e04ed6bb45bab7f265556da02e9e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:15:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-38a7e04ed6bb45bab7f265556da02e9e2023-12-03T12:44:58ZengMDPI AGSensors1424-82202021-02-01214116610.3390/s21041166Autonomous Identification and Positioning of Trucks during Collaborative Forage HarvestingWei Zhang0Liang Gong1Suyue Chen2Wenjie Wang3Zhonghua Miao4Chengliang Liu5School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaIn 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.https://www.mdpi.com/1424-8220/21/4/1166agricultural automationforage harvestercollaborative unloading operationidentification and positioningvisual odometryrandom sample consensus |
| spellingShingle | Wei Zhang Liang Gong Suyue Chen Wenjie Wang Zhonghua Miao Chengliang Liu Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting Sensors agricultural automation forage harvester collaborative unloading operation identification and positioning visual odometry random sample consensus |
| title | Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting |
| title_full | Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting |
| title_fullStr | Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting |
| title_full_unstemmed | Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting |
| title_short | Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting |
| title_sort | autonomous identification and positioning of trucks during collaborative forage harvesting |
| topic | agricultural automation forage harvester collaborative unloading operation identification and positioning visual odometry random sample consensus |
| url | https://www.mdpi.com/1424-8220/21/4/1166 |
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