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Cited by 338Robot Manipulation and LearningRobotics and Automated SystemsFlexible and Reconfigurable Manufacturing Systems

Smart industrial robot control trends, challenges and opportunities within manufacturing

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Arents, Janis, Greitans, Modris·2022·Applied Sciences
DOI 10.3390/app12020937
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Abstract

Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility and deep understanding of complex manufacturing processes are becoming the key advantage to raise competitiveness. This review first expresses the significance of smart industrial robot control in manufacturing towards future factories by listing the needs, requirements and introducing the envisioned concept of smart industrial robots. Secondly, the current trends that are based on different learning strategies and methods are explored. Current computer-vision, deep reinforcement learning and imitation learning based robot control approaches and possible applications in manufacturing are investigated. Gaps, challenges, limitations and open issues are identified along the way.

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Authors
Arents, J., & Greitans, M.
Affiliations
Institute of Electronics and Computer Science, 14 Dzerbenes St., LV-1006 Riga, Latvia
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2022-01-17
Volume
12
Issue
2
Pages
937
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app12020937
OpenAlex
W4205837126
Cited by
338
References
6
Citations by year
2026: 71 · 2025: 110 · 2024: 81 · 2023: 48 · 2022: 28
FWCI
26.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
The work has been performed in the project AI4DI: Artificial Intelligence for Digitizing Industry, under grant agreement No 826060. The project is co-funded by grants from Germany, Austria, Finland, France, Norway, Latvia, Belgium, Italy, Switzerland, and (Grant agreement No 826060)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
Keywords
industrial robot control, cognitive robots, artificial intelligence in manufacturing, deep reinforcement learning, imitation learning, computer vision, learning control, production flexibility, factory of the future

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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