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contributor authorKurrek, Philip
contributor authorZoghlami, Firas
contributor authorJocas, Mark
contributor authorStoelen, Martin
contributor authorSalehi, Vahid
date accessioned2022-02-04T22:07:13Z
date available2022-02-04T22:07:13Z
date copyright6/9/2020 12:00:00 AM
date issued2020
identifier issn1530-9827
identifier otherjcise_20_6_061006.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4274914
description abstractThe increasing individualization of products reinforces the importance of decoupled factories in production processes. Artificial intelligence (AI) is a recognized technology for problem solving and accelerates automation by enabling systems to act independently. In the field of robotics, there are new deep learning approaches which make robotic control systems human independent. This work provides a literature overview of the current state of development methodologies, showing that there are only limited methods available for the development of artificial intelligent robots. We present a novel development methodology based on artificial intelligence, particularly deep reinforcement learning. The so-called Q-model can enable robots to learn specific tasks independently. In summary, we show how an AI-based methodology assists the development of autonomous robots along the product lifecycle.
publisherThe American Society of Mechanical Engineers (ASME)
titleQ-Model: An Artificial Intelligence Based Methodology for the Development of Autonomous Robots
typeJournal Paper
journal volume20
journal issue6
journal titleJournal of Computing and Information Science in Engineering
identifier doi10.1115/1.4046992
journal fristpage061006-1
journal lastpage061006-9
page9
treeJournal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006
contenttypeFulltext


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