Adaptive acquisition planning for visual inspection in remanufacturing using reinforcement learning Article Swipe
Jan-Philipp Kaiser
,
Jonas Gäbele
,
Dominik Koch
,
Jonas Schmid
,
Florian Stamer
,
Gisela Lanza
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10845-024-02478-0
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10845-024-02478-0
In remanufacturing, humans perform visual inspection tasks manually. In doing so, human inspectors implicitly solve variants of visual acquisition planning problems. Nowadays, solutions to these problems are computed based on the object geometry of the object to be inspected. In remanufacturing, however, there are often many product variants, and the existence of geometric object models cannot be assumed. This makes it difficult to plan and solve visual acquisition planning problems for the automated execution of visual inspection tasks. Reinforcement learning offers the possibility of learning and reproducing human inspection behavior and solving the visual inspection problem, even for problems in which no object geometry is available. To investigate reinforcement learning as a solution, a simple simulation environment is developed, allowing the execution of reproducible and controllable experiments. Different reinforcement learning agent modeling alternatives are developed and compared for solving the derived visual planning problems. The results of this work show that reinforcement learning agents can solve the derived visual planning problems in use cases without available object geometry by using domain-specific prior knowledge. Our proposed framework is available open source under the following link: https://github.com/Jarrypho/View-Planning-Simulation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10845-024-02478-0
- OA Status
- hybrid
- Cited By
- 5
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401898118
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4401898118Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10845-024-02478-0Digital Object Identifier
- Title
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Adaptive acquisition planning for visual inspection in remanufacturing using reinforcement learningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-27Full publication date if available
- Authors
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Jan-Philipp Kaiser, Jonas Gäbele, Dominik Koch, Jonas Schmid, Florian Stamer, Gisela LanzaList of authors in order
- Landing page
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https://doi.org/10.1007/s10845-024-02478-0Publisher 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/s10845-024-02478-0Direct OA link when available
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Remanufacturing, Reinforcement learning, Reinforcement, Manufacturing engineering, Production planning, Production (economics), Computer science, Engineering, Artificial intelligence, Structural engineering, Economics, MacroeconomicsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 5Per-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/W2784996692, https://openalex.org/W1993521546, https://openalex.org/W3107664567, https://openalex.org/W2016123536, https://openalex.org/W2096268286, https://openalex.org/W1973489790, https://openalex.org/W2018995617, https://openalex.org/W2613333231, https://openalex.org/W4252497636, https://openalex.org/W4232713319, https://openalex.org/W2162625211, https://openalex.org/W2781726626, https://openalex.org/W2026891605, https://openalex.org/W2066272284, https://openalex.org/W3034584726, https://openalex.org/W2893889064, https://openalex.org/W2074643569, https://openalex.org/W3046033366, https://openalex.org/W4211089519, https://openalex.org/W4235128656, https://openalex.org/W2955103819, https://openalex.org/W3042915245, https://openalex.org/W2767994151, https://openalex.org/W3137986185, https://openalex.org/W1968268217, https://openalex.org/W3009018472, https://openalex.org/W2145339207, https://openalex.org/W3134124573, https://openalex.org/W169931978, https://openalex.org/W4205415842, https://openalex.org/W3201000358, https://openalex.org/W2767050701, https://openalex.org/W3184522896, https://openalex.org/W3126119618, https://openalex.org/W2560609797, https://openalex.org/W3216772467, https://openalex.org/W2020307908, https://openalex.org/W3139411728, https://openalex.org/W1990269173, https://openalex.org/W2735340665, https://openalex.org/W2092705708, https://openalex.org/W2078505501, https://openalex.org/W4313537271, https://openalex.org/W2766447205, https://openalex.org/W148127579, https://openalex.org/W6677916085, https://openalex.org/W2739105800, https://openalex.org/W3121793769, https://openalex.org/W4237657127, https://openalex.org/W3035014292, https://openalex.org/W3034493208, https://openalex.org/W4226457765, https://openalex.org/W2886499109, https://openalex.org/W3046735138, https://openalex.org/W3101442004, https://openalex.org/W3124220725, https://openalex.org/W4214717370 |
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| corresponding_author_ids | https://openalex.org/A5007908695 |
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| corresponding_institution_ids | https://openalex.org/I102335020 |
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