Robust Framework for intelligent Gripping Point Detection

Poss, Christian; Mlouka, Ons Ben; Irrenhauser, Thomas; Prueglmeier, Marco; Goehring, Daniel; Zoghlami, Firas; Salehi, Vahid

doi:10.1109/iecon.2019.8927308

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Even though the main focus of object detection algorithm development historically has been addressed in different fields [33], the need for such systems is also present in the smart manufacturing context to deal with uncertainties of the environment. Thus, such deep learning-based approaches have proven to be quite flexible if conditions changes [34]. Object detection in industrial robotic tasks differs from other tasks as extracted information needs to be more detailed and precise in order to successfully manipulate objects.…”

Section: Computer Vision-based Controlmentioning

confidence: 99%

Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

2022

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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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Section: Computer Vision-based Controlmentioning

confidence: 99%

Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

2022

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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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AI Models and Methods in Automotive Manufacturing: A Systematic Literature Review

Mueller

Mezhuyev

2022

Studies in Computational Intelligence

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Robust Framework for intelligent Gripping Point Detection

Cited by 3 publications

References 2 publications

Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

Design of a Deep Post Gripping Perception Framework for Industrial Robots

AI Models and Methods in Automotive Manufacturing: A Systematic Literature Review

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