| contributor author | Kurrek, Philip | |
| contributor author | Zoghlami, Firas | |
| contributor author | Jocas, Mark | |
| contributor author | Stoelen, Martin | |
| contributor author | Salehi, Vahid | |
| date accessioned | 2022-02-04T22:07:13Z | |
| date available | 2022-02-04T22:07:13Z | |
| date copyright | 6/9/2020 12:00:00 AM | |
| date issued | 2020 | |
| identifier issn | 1530-9827 | |
| identifier other | jcise_20_6_061006.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4274914 | |
| description abstract | The 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. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Q-Model: An Artificial Intelligence Based Methodology for the Development of Autonomous Robots | |
| type | Journal Paper | |
| journal volume | 20 | |
| journal issue | 6 | |
| journal title | Journal of Computing and Information Science in Engineering | |
| identifier doi | 10.1115/1.4046992 | |
| journal fristpage | 061006-1 | |
| journal lastpage | 061006-9 | |
| page | 9 | |
| tree | Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006 | |
| contenttype | Fulltext | |