An obstacle separation method for robotic picking of fruits in clusters Article Swipe
Ya Xiong
,
Yuanyue Ge
,
Pål Johan From
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.compag.2020.105397
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.compag.2020.105397
Selectively picking a target fruit surrounded by obstacles is one of the major challenges for fruit harvesting robots. Different from traditional obstacle avoidance methods, this paper presents an active obstacle separation strategy that combines push and drag motions based on 3D visual perception to separate obstacles from the target. We define a region of interest 3D point cloud with a number of sub-blocks around the target to determine the presence or absence of obstacles and generate the separation paths accordingly. A linear push is used to clear the obstacles from the area below the target, while a zig-zag push that contains several linear motions is proposed to push aside more dense obstacles. The zig-zag push can generate multi-directional pushes and the side-to-side motion can break the static contact force between the target and obstacles, thus helping the gripper to receive a target in more complex situations. Moreover, we propose a novel drag operation to address the issue of mis-capturing obstacles located above the target, in which the gripper drags the target to a place with fewer obstacles and then pushes back to move the obstacles aside for further detachment. Furthermore, an image processing pipeline consisting of color thresholding, object detection using deep learning and point cloud operation, is developed to implement the proposed method on a newly developed harvesting robot. Field tests show that the proposed method can improve the picking performance substantially. This method helps to enable complex clusters of fruits to be harvested with a higher success rate than conventional methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.compag.2020.105397
- https://www.sciencedirect.com/science/article/pii/S0168169919324214?via%3Dihub
- OA Status
- hybrid
- Cited By
- 47
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3034999307
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3034999307Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.compag.2020.105397Digital Object Identifier
- Title
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An obstacle separation method for robotic picking of fruits in clustersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-06-15Full publication date if available
- Authors
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Ya Xiong, Yuanyue Ge, Pål Johan FromList of authors in order
- Landing page
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https://doi.org/10.1016/j.compag.2020.105397Publisher landing page
- PDF URL
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https://www.sciencedirect.com/science/article/pii/S0168169919324214?via%3DihubDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://www.sciencedirect.com/science/article/pii/S0168169919324214?via%3DihubDirect OA link when available
- Concepts
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Obstacle, Artificial intelligence, Thresholding, Computer vision, Robot, Point cloud, Computer science, Point (geometry), Pipeline (software), Drag, Engineering, Image (mathematics), Mathematics, Geography, Geometry, Aerospace engineering, Programming language, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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47Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 12, 2023: 12, 2022: 9, 2021: 5Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2528895427, https://openalex.org/W2900657351, https://openalex.org/W6757268131, https://openalex.org/W4211241511, https://openalex.org/W2979404229, https://openalex.org/W6636015352, https://openalex.org/W6729419724, https://openalex.org/W2770506509, https://openalex.org/W2164626235, https://openalex.org/W2968952932, https://openalex.org/W2911010841, https://openalex.org/W6730939875, https://openalex.org/W1971365953, https://openalex.org/W6749776752, https://openalex.org/W2551182329, https://openalex.org/W2963566599, https://openalex.org/W605243474, https://openalex.org/W2562021215, https://openalex.org/W4253642873, https://openalex.org/W2591965935, https://openalex.org/W1603219075, https://openalex.org/W4252370303, https://openalex.org/W2904207885, https://openalex.org/W1597612847, https://openalex.org/W2962793652 |
| referenced_works_count | 25 |
| abstract_inverted_index.A | 80 |
| abstract_inverted_index.a | 2, 51, 59, 96, 140, 149, 172, 215, 246 |
| abstract_inverted_index.3D | 40, 55 |
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| abstract_inverted_index.is | 8, 83, 104, 207 |
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| abstract_inverted_index.we | 147 |
| abstract_inverted_index.The | 112 |
| abstract_inverted_index.and | 35, 74, 119, 132, 177, 203 |
| abstract_inverted_index.can | 115, 123, 227 |
| abstract_inverted_index.for | 14, 186 |
| abstract_inverted_index.one | 9 |
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| abstract_inverted_index.image | 191 |
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| abstract_inverted_index.target. | 48 |
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| abstract_inverted_index.implement | 210 |
| abstract_inverted_index.obstacles | 7, 45, 73, 88, 159, 176, 184 |
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| abstract_inverted_index.perception | 42 |
| abstract_inverted_index.processing | 192 |
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| abstract_inverted_index.surrounded | 5 |
| abstract_inverted_index.Selectively | 0 |
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| abstract_inverted_index.thresholding, | 197 |
| abstract_inverted_index.substantially. | 232 |
| abstract_inverted_index.multi-directional | 117 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5042074023 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I54108979 |
| citation_normalized_percentile.value | 0.96014555 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |