Memory Efficient Deep Learning-Based Grasping Point Detection of Nontrivial Objects for Robotic Bin Picking Article Swipe
Petr Doležel
,
Dominik Štursa
,
Dušan Kopecký
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10846-024-02153-9
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10846-024-02153-9
Picking up non-trivial objects from a bin with a robotic arm is a common task of modern industrial processes. Here, an efficient data-driven method of grasping point detection, based on an attention squeeze parallel U-shaped neural network (ASP U-Net) for the bin picking task, is proposed. The method directly provides all necessary information about the feasible grasping points of objects, which are randomly or regularly arranged in a bin with side walls. Moreover, the method is able to evaluate and select the optimal grasping point among the feasible ones for two types of end effectors, i.e., a vacuum cup and a parallel gripper. The key element of the utilized ASP U-Net neural network is the transformation of a single RGB-Depth image of the bin containing nontrivial objects into a schematic grey-scale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. The experiments carried out in this study include a comprehensive set of scenes with randomly scattered, ordered, and semi-ordered objects arranged in impeccable or deformed bins. The results indicate outstanding accuracy with more than acceptable computational requirements. Additionally, the scaling possibilities of the method can offer extremely lightweight implementations, applicable, for example, to battery-powered edge-computing devices with low RAM capacity.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10846-024-02153-9
- OA Status
- hybrid
- References
- 34
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- 10
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https://openalex.org/W4401028776Canonical identifier for this work in OpenAlex
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https://doi.org/10.1007/s10846-024-02153-9Digital Object Identifier
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Memory Efficient Deep Learning-Based Grasping Point Detection of Nontrivial Objects for Robotic Bin PickingWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-07-25Full publication date if available
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Petr Doležel, Dominik Štursa, Dušan KopeckýList of authors in order
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https://doi.org/10.1007/s10846-024-02153-9Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1007/s10846-024-02153-9Direct OA link when available
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Artificial intelligence, Bin, Computer science, Point (geometry), Deep learning, Computer vision, Mathematics, Algorithm, GeometryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.gradient | 145 |
| abstract_inverted_index.grasping | 26, 57, 84, 140 |
| abstract_inverted_index.gripper. | 103 |
| abstract_inverted_index.indicate | 176 |
| abstract_inverted_index.objects, | 60 |
| abstract_inverted_index.ordered, | 164 |
| abstract_inverted_index.parallel | 34, 102 |
| abstract_inverted_index.provides | 50 |
| abstract_inverted_index.randomly | 63, 162 |
| abstract_inverted_index.utilized | 109 |
| abstract_inverted_index.Moreover, | 73 |
| abstract_inverted_index.RGB-Depth | 120 |
| abstract_inverted_index.attention | 32 |
| abstract_inverted_index.capacity. | 207 |
| abstract_inverted_index.efficient | 22 |
| abstract_inverted_index.extremely | 194 |
| abstract_inverted_index.geometric | 146 |
| abstract_inverted_index.necessary | 52 |
| abstract_inverted_index.positions | 135 |
| abstract_inverted_index.proposed. | 46 |
| abstract_inverted_index.regularly | 65 |
| abstract_inverted_index.schematic | 130 |
| abstract_inverted_index.acceptable | 182 |
| abstract_inverted_index.containing | 125 |
| abstract_inverted_index.detection, | 28 |
| abstract_inverted_index.effectors, | 95 |
| abstract_inverted_index.grey-scale | 131 |
| abstract_inverted_index.impeccable | 170 |
| abstract_inverted_index.industrial | 18 |
| abstract_inverted_index.nontrivial | 126 |
| abstract_inverted_index.processes. | 19 |
| abstract_inverted_index.scattered, | 163 |
| abstract_inverted_index.applicable, | 197 |
| abstract_inverted_index.data-driven | 23 |
| abstract_inverted_index.experiments | 149 |
| abstract_inverted_index.information | 53 |
| abstract_inverted_index.lightweight | 195 |
| abstract_inverted_index.non-trivial | 3 |
| abstract_inverted_index.outstanding | 177 |
| abstract_inverted_index.semi-ordered | 166 |
| abstract_inverted_index.Additionally, | 185 |
| abstract_inverted_index.comprehensive | 157 |
| abstract_inverted_index.computational | 183 |
| abstract_inverted_index.possibilities | 188 |
| abstract_inverted_index.requirements. | 184 |
| abstract_inverted_index.edge-computing | 202 |
| abstract_inverted_index.transformation | 116 |
| abstract_inverted_index.battery-powered | 201 |
| abstract_inverted_index.implementations, | 196 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5008067428 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I140744787 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.14909439 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |