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Laser-range-finder-based target detection for human-robot collaboration in hilly orchards.

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Title: Laser-range-finder-based target detection for human-robot collaboration in hilly orchards.
Authors: Xiulan Bao1 orchidbaoxl@mail.hzau.edu.cn, Xiaojie Ma1 mxj3023@163.com, Yuxin Niu2 niuyxhit@163.com, Qilin Yin1 Y931839413@163.com, Hong Chen1 chenhong@mail.hzau.edu.cn, Qing Wu1 wuqing@mail.bzau.cdu.cn
Source: International Journal of Agricultural & Biological Engineering. Apr2025, Vol. 18 Issue 2, p231-238. 8p.
Subjects: Tree pruning, Vector data, Manual labor, Point cloud, Torso
Abstract: Human-robot collaboration is a promising means to promote orchard intelligence and reduce the over-reliance on manual work for complex agronomic practices such as fruit tree pruning, flower and fruit thinning, and harvesting. Accurate target detection and recognition of robots on humans are the basis and prerequisite for subsequent autonomous human-robot collaboration. In this study, detection and recognition of following robots for human torso were carried out in a standardized hilly orchard. A LiDAR-based human torso detection method was proposed based on the actual orchard environment. Breakpoint detection was used to cluster and segment the point clouds, and the segmentation thresholds were determined based on experimental results. The geometric attributes of the human torso were trained in the classification detection model, resulting in the extraction of six geometric attributes of the human torso. The classification model was then trained with various combinations to obtain the optimal feature combination [girth-depth-average curvature (G-D-k)] for human torso recognition in an orchard environment. Practical experiments were carried out to validate the feasibility and accuracy of the G-D-k feature combination. The experimental results demonstrate that the G-D-k feature combination can accurately recognize human bodies in orchards. The LiDAR-based detection method can achieve relatively accurate human detection and recognition in complex orchard environments, providing a reference for target detection in human-robot collaboration in orchards. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Agricultural & Biological Engineering is the property of International Journal of Agricultural & Biological Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 185319307
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  Data: Laser-range-finder-based target detection for human-robot collaboration in hilly orchards.
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  Data: <searchLink fieldCode="AR" term="%22Xiulan+Bao%22">Xiulan Bao</searchLink><relatesTo>1</relatesTo><i> orchidbaoxl@mail.hzau.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiaojie+Ma%22">Xiaojie Ma</searchLink><relatesTo>1</relatesTo><i> mxj3023@163.com</i><br /><searchLink fieldCode="AR" term="%22Yuxin+Niu%22">Yuxin Niu</searchLink><relatesTo>2</relatesTo><i> niuyxhit@163.com</i><br /><searchLink fieldCode="AR" term="%22Qilin+Yin%22">Qilin Yin</searchLink><relatesTo>1</relatesTo><i> Y931839413@163.com</i><br /><searchLink fieldCode="AR" term="%22Hong+Chen%22">Hong Chen</searchLink><relatesTo>1</relatesTo><i> chenhong@mail.hzau.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Qing+Wu%22">Qing Wu</searchLink><relatesTo>1</relatesTo><i> wuqing@mail.bzau.cdu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Agricultural+%26+Biological+Engineering%22">International Journal of Agricultural & Biological Engineering</searchLink>. Apr2025, Vol. 18 Issue 2, p231-238. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Tree+pruning%22">Tree pruning</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+data%22">Vector data</searchLink><br /><searchLink fieldCode="DE" term="%22Manual+labor%22">Manual labor</searchLink><br /><searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Torso%22">Torso</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Human-robot collaboration is a promising means to promote orchard intelligence and reduce the over-reliance on manual work for complex agronomic practices such as fruit tree pruning, flower and fruit thinning, and harvesting. Accurate target detection and recognition of robots on humans are the basis and prerequisite for subsequent autonomous human-robot collaboration. In this study, detection and recognition of following robots for human torso were carried out in a standardized hilly orchard. A LiDAR-based human torso detection method was proposed based on the actual orchard environment. Breakpoint detection was used to cluster and segment the point clouds, and the segmentation thresholds were determined based on experimental results. The geometric attributes of the human torso were trained in the classification detection model, resulting in the extraction of six geometric attributes of the human torso. The classification model was then trained with various combinations to obtain the optimal feature combination [girth-depth-average curvature (G-D-k)] for human torso recognition in an orchard environment. Practical experiments were carried out to validate the feasibility and accuracy of the G-D-k feature combination. The experimental results demonstrate that the G-D-k feature combination can accurately recognize human bodies in orchards. The LiDAR-based detection method can achieve relatively accurate human detection and recognition in complex orchard environments, providing a reference for target detection in human-robot collaboration in orchards. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Agricultural & Biological Engineering is the property of International Journal of Agricultural & Biological Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.25165/j.ijabe.20251802.8346
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 231
    Subjects:
      – SubjectFull: Tree pruning
        Type: general
      – SubjectFull: Vector data
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      – SubjectFull: Manual labor
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      – SubjectFull: Point cloud
        Type: general
      – SubjectFull: Torso
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    Titles:
      – TitleFull: Laser-range-finder-based target detection for human-robot collaboration in hilly orchards.
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            NameFull: Xiulan Bao
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            NameFull: Xiaojie Ma
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            NameFull: Yuxin Niu
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            NameFull: Qilin Yin
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            NameFull: Hong Chen
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            NameFull: Qing Wu
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            – D: 01
              M: 04
              Text: Apr2025
              Type: published
              Y: 2025
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            – TitleFull: International Journal of Agricultural & Biological Engineering
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