| Project/Area Number |
16K00310
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Aichi University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
伊藤 暢浩 愛知工業大学, 情報科学部, 教授 (40314075)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | エージェント / 災害救助シミュレーション / 地図分析 / 開発フレームワーク / VR / ロボカップレスキューシミュレーション / 大規模災害救助シミュレーション / マルチエージェントシステム / 人工知能 |
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| Outline of Final Research Achievements |
We focus on the RoboCupRescue Simulation (RRS) project. It has been implemented as one of the responses to recent large-scale natural disasters. In particular, the project provides a platform for assessing disaster-relief agents and simulations.
In this study, we implemented the mac creation tool that creates maps used in RRS from OpenStreetMap. The RRS needs map data for various regions because this is important for the disaster relief simulator to enable simulations in these regions. However, RRS only provides a few maps because its map creation tool is not adequate for creating maps of various regions. In addition, we proposed a combination of an agent development framework and experiment management software. The RRS research evolution is limited because all agents’ programs must be developed by each researcher and the experimental operations are complex.
The proposed tool and software solved the problem and is used all RRS participants from all over the world.
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| Academic Significance and Societal Importance of the Research Achievements |
近い将来において大規模な地震災害がおきたときに，救助ロボットが導入されることが考えられる．その際に本研究成果を活かすことで，被害の広がりを最小限に食い止められることが期待できる．本研究課題では，様々なアルゴリズムを実装されたエージェントがどのような特徴を持つ地域で有効に活動できるかを分析している．これは，同じ規模の災害がおきたとしても，都市の建物の配置や道路網の形状によって最適な救助活動が異なる．そのため，それぞれの都市の形状に応じた救助ロボットの活動が求められる．それを本研究では明らかにすることを目指しており，学術的にも社会的にも非常に意義がある．
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