Enabling Robot Selective Trained Deep Neural Networks for Object Detection Through Intelligent Infrastructure

Poss, Christian; Irrenhauser, Thomas; Prueglmeier, Marco; Goehring, Daniel; Zoghlami, Firas; Salehi, Vahid; Ibragimov, Olimjon

doi:10.1145/3351917.3351982

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…The focus in the research field has been on the perception for the gripping step [18][19][20] to the detriment of the placing action [21,22]. Analogically, the focus of the industrial research and development lies in the perception based gripping of items in structured or unstructured environments [23,24].…”

Section: Introductionmentioning

confidence: 99%

Design of a Deep Post Gripping Perception Framework for Industrial Robots

Zoghlami

Kurrek

Jocas

et al. 2020

Journal of Computing and Information Science in Engineering

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The use of flexible and autonomous robotic systems is the solution for the automation in dynamic and unstructured industrial environments. This context requires the robot to be aware of its surroundings throughout the whole manipulation task, also after accomplishing the gripping action. This work introduces the deep post gripping perception framework, which includes the post gripping perception abilities realized with the help of deep learning techniques, especially unsupervised learning methods. These abilities help the robot to execute a stable and precise placing of the gripped items depending on the required task. We describe the development of the framework based on an established literature review. The result of the work is a modular design of the framework with the help of three functional components used to build planning, monitoring and verifying modules.

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Section: Introductionmentioning

confidence: 99%

Design of a Deep Post Gripping Perception Framework for Industrial Robots

Zoghlami

Kurrek

Jocas

et al. 2020

Journal of Computing and Information Science in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

MLOps Challenges in Industry 4.0

Faubel,

Schmid,

Eichelberger

2023

SN COMPUT. SCI.

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An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is only little research focusing on MLOps for Industry 4.0. In this paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We provide an initial analysis of MLOps approaches and identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.

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Enabling Robot Selective Trained Deep Neural Networks for Object Detection Through Intelligent Infrastructure

Cited by 2 publications

References 4 publications

Design of a Deep Post Gripping Perception Framework for Industrial Robots

Design of a Deep Post Gripping Perception Framework for Industrial Robots

MLOps Challenges in Industry 4.0

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