Dynamic Scene Graph for Mutual-Cognition Generation in Proactive Human-Robot Collaboration Article Swipe
Shufei Li
,
Pai Zheng
,
Zuoxu Wang
,
Junming Fan
,
Lihui Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.procir.2022.05.089
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.procir.2022.05.089
Human-robot collaboration (HRC) plays a crucial role in agile, flexible, and human-centric manufacturing towards the mass personalization transition. Nevertheless, in today's HRC tasks, either humans or robots need to follow the partners' commands and instructions along collaborative activities progressing, instead of proactive, mutual engagement. The non-semantic perception of HRC scenarios impedes mutually needed, proactive planning and high-cognitive capabilities in existing HRC systems. To overcome the bottleneck, this research explores a dynamic scene graph-based method for mutual-cognition generation in Proactive HRC applications. Firstly, a spatial-attention object detector is utilized to dynamically perceive objects in industrial settings. Secondly, a linking prediction module is leveraged to construct HRC scene graphs. An attentional graph convolutional network (GCN) is utilized to capture relations between industrial parts, human operators, and robot operations and reason structural connections of human-robot collaborative processing as graph embedding, which links to mutual planners for human operation supports and robot proactive instructions. Lastly, the Proactive HRC implementation is demonstrated on disassembly tasks of aging electronic vehicle batteries (EVBs) and evaluate its mutual-cognition capabilities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procir.2022.05.089
- OA Status
- diamond
- Cited By
- 15
- References
- 24
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4285265289Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.procir.2022.05.089Digital Object Identifier
- Title
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Dynamic Scene Graph for Mutual-Cognition Generation in Proactive Human-Robot CollaborationWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
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2022-01-01Full publication date if available
- Authors
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Shufei Li, Pai Zheng, Zuoxu Wang, Junming Fan, Lihui WangList of authors in order
- Landing page
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https://doi.org/10.1016/j.procir.2022.05.089Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.procir.2022.05.089Direct OA link when available
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Bottleneck, Human–computer interaction, Computer science, Robot, Cognition, Human–robot interaction, Graph, Artificial intelligence, Psychology, Embedded system, Neuroscience, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 5, 2024: 3, 2023: 5, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3205172848, https://openalex.org/W4200449846, https://openalex.org/W2955808192, https://openalex.org/W3217651858, https://openalex.org/W3088667626, https://openalex.org/W3204200719, https://openalex.org/W3024513037, https://openalex.org/W3046216614, https://openalex.org/W3183211010, https://openalex.org/W3034213471, https://openalex.org/W2886970679, https://openalex.org/W2094537981, https://openalex.org/W3128650182, https://openalex.org/W2890106798, https://openalex.org/W3159728644, https://openalex.org/W3051279352, https://openalex.org/W3089045049, https://openalex.org/W2888519266, https://openalex.org/W3111182193, https://openalex.org/W2887665961, https://openalex.org/W2963349093, https://openalex.org/W3207626483, https://openalex.org/W2969679616, https://openalex.org/W4206654130 |
| referenced_works_count | 24 |
| abstract_inverted_index.a | 4, 69, 82, 96 |
| abstract_inverted_index.An | 107 |
| abstract_inverted_index.To | 62 |
| abstract_inverted_index.as | 134 |
| abstract_inverted_index.in | 7, 19, 58, 77, 92 |
| abstract_inverted_index.is | 86, 100, 113, 155 |
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| abstract_inverted_index.on | 157 |
| abstract_inverted_index.or | 25 |
| abstract_inverted_index.to | 28, 88, 102, 115, 139 |
| abstract_inverted_index.HRC | 21, 48, 60, 79, 104, 153 |
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| abstract_inverted_index.and | 10, 33, 55, 123, 126, 146, 166 |
| abstract_inverted_index.for | 74, 142 |
| abstract_inverted_index.its | 168 |
| abstract_inverted_index.the | 14, 30, 64, 151 |
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| abstract_inverted_index.humans | 24 |
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| abstract_inverted_index.module | 99 |
| abstract_inverted_index.mutual | 42, 140 |
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| abstract_inverted_index.robots | 26 |
| abstract_inverted_index.tasks, | 22 |
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| abstract_inverted_index.capture | 116 |
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| abstract_inverted_index.graphs. | 106 |
| abstract_inverted_index.impedes | 50 |
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| abstract_inverted_index.needed, | 52 |
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| abstract_inverted_index.towards | 13 |
| abstract_inverted_index.vehicle | 163 |
| abstract_inverted_index.Firstly, | 81 |
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| abstract_inverted_index.detector | 85 |
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| abstract_inverted_index.explores | 68 |
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| abstract_inverted_index.research | 67 |
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| abstract_inverted_index.flexible, | 9 |
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| abstract_inverted_index.partners' | 31 |
| abstract_inverted_index.proactive | 53, 148 |
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| abstract_inverted_index.scenarios | 49 |
| abstract_inverted_index.settings. | 94 |
| abstract_inverted_index.activities | 37 |
| abstract_inverted_index.electronic | 162 |
| abstract_inverted_index.embedding, | 136 |
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| abstract_inverted_index.industrial | 93, 119 |
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| abstract_inverted_index.operators, | 122 |
| abstract_inverted_index.perception | 46 |
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| abstract_inverted_index.capabilities. | 170 |
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| abstract_inverted_index.personalization | 16 |
| abstract_inverted_index.mutual-cognition | 75, 169 |
| abstract_inverted_index.spatial-attention | 83 |
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| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5079101040 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I14243506 |
| citation_normalized_percentile.value | 0.88222528 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |