| Project/Area Number |
11450060
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| Research Category |
Grant-in-Aid for Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
機械工作・生産工学
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| Research Institution | KOBE UNIVERSITY |
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Principal Investigator |
UEDA Kanji Kobe University, Professor, 工学部, 教授 (50031133)
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| Co-Investigator(Kenkyū-buntansha) |
OHKURA Kazuhiro Kobe University, Associate Professor, 工学部, 助教授 (40252788)
HATONO Itsuo Kobe University, Professor, 総合情報処理センター, 助教授 (10208548)
ヤリ ワーリオ ノキア, ジャパン・ノキアリサーチセンター, 研究室長
FUJII Nobutada Kobe University, Professor, 工学部, 助手
VAARIO Jari Kobe University, Professor
ミハイル シビニン 神戸大学, 工学部, 助教授 (90274125)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥10,900,000 (Direct Cost: ¥10,900,000)
Fiscal Year 2000: ¥5,100,000 (Direct Cost: ¥5,100,000)
Fiscal Year 1999: ¥5,800,000 (Direct Cost: ¥5,800,000)
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| Keywords | Interactive Manufacturing / Biological Manufacturing / virtual space / self-organization / reinforcement learning / インタラクティブ環境 / 人工現実感 |
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| Research Abstract |
This study can be concluded as following :
1. Framework of Interactive Manufacturing Systems
Firstly, the difficulties of problems in the current manufacturing have been classified. To overcome the most difficult class, Class III, Interactive Manufacturing Systems has been proposed. To use virtual space has been proposed in order to realize Interactive Manufacturing, and the relationship between virtual and real spaces has been theoretically concerned.
2. Interactive Environment Using Virtual Space
The method to connect self-organization simulation with virtual space has been proposed to realize an interactive environment. The effectiveness of the proposed method has been verified, because interactions between artifacts and human in the virtual pace have been able to be observed in the experiments.
3. Manufacturing Systems Using Reinforcement Learning
To realize interactions between artifacts and human in the Interactive Manufacturing, the artifacts should be able to adapt to the action by human. In this study, manufacturing system using reinforcement learning, where the objective can be achieved as the result of local interactions between autonomous elements, has been proposed. Because the objectives, minimizing deviation from due date and maximizing throughput, have been achieved in the experiments, the effectiveness of the proposed method has been verified.
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