Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows
The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8807 |
| _version_ | 1797631289567739904 |
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| author | Baoxing Gu Qin Liu Yi Gao Guangzhao Tian Baohua Zhang Haiqing Wang He Li |
| author_facet | Baoxing Gu Qin Liu Yi Gao Guangzhao Tian Baohua Zhang Haiqing Wang He Li |
| author_sort | Baoxing Gu |
| collection | DOAJ |
| description | The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. First, the fruit tree trunk is identified based on the improved YOLOv4 model; second, the camera coordinates of the tree trunk are calculated using the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined, the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall rate of the improved YOLOv4 model for fruit tree trunk detection are 5.92% and 7.91% higher, respectively, than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows and provide a reference for the autonomous visual navigation of orchard robots. |
| first_indexed | 2024-03-11T11:21:43Z |
| format | Article |
| id | doaj.art-c89d4311b2f14214af8935d7be3d94fa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:43Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c89d4311b2f14214af8935d7be3d94fa2023-11-10T15:12:09ZengMDPI AGSensors1424-82202023-10-012321880710.3390/s23218807Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree RowsBaoxing Gu0Qin Liu1Yi Gao2Guangzhao Tian3Baohua Zhang4Haiqing Wang5He Li6College of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing 210096, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaThe relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. First, the fruit tree trunk is identified based on the improved YOLOv4 model; second, the camera coordinates of the tree trunk are calculated using the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined, the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall rate of the improved YOLOv4 model for fruit tree trunk detection are 5.92% and 7.91% higher, respectively, than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows and provide a reference for the autonomous visual navigation of orchard robots.https://www.mdpi.com/1424-8220/23/21/8807orchard robotautonomous navigationpositional parametersmachine visionYOLO |
| spellingShingle | Baoxing Gu Qin Liu Yi Gao Guangzhao Tian Baohua Zhang Haiqing Wang He Li Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows Sensors orchard robot autonomous navigation positional parameters machine vision YOLO |
| title | Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows |
| title_full | Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows |
| title_fullStr | Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows |
| title_full_unstemmed | Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows |
| title_short | Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows |
| title_sort | research on the relative position detection method between orchard robots and fruit tree rows |
| topic | orchard robot autonomous navigation positional parameters machine vision YOLO |
| url | https://www.mdpi.com/1424-8220/23/21/8807 |
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