| Project/Area Number |
16F16701
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Research Field |
Intelligent informatics
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
吉田 英一 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究部門付 (30358329)
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| Co-Investigator(Kenkyū-buntansha) |
ORTHEY ANDREAS 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 外国人特別研究員
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| Project Period (FY) |
2016-11-07 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2018: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2017: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2016: ¥500,000 (Direct Cost: ¥500,000)
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| Keywords | 動作計画 / 商空間 / ロボット / 知能ロボティクス / ヒューマノイド / 知能ロボット |
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| Outline of Annual Research Achievements |
We have developed a new motion planning framework using the mathematical concept of a quotient-space, through removal of a dimension to improve the efficiency of computation. A quotient-space is a simplified space, which is created by declaring points in a space as being equivalent, and then grouping them together into a single point of the quotient-space. We realized this simplification through the idea of nesting robots in each other.
Our approach is general to be applied to any robot. Fo a manipulator arm, we nest a series of lower-dimensional manipulator arms inside the original arm, whereby each lower-dimensional arm is created by removing a link of the arm. This nesting of robots creates a series of quotient-spaces, which are all nested inside the original configuration space.
We have subsequently developed a new algorithm, called the quotient-space roadmap planner (QMP), which is able to exploit quotient-spaces. Our planner is unique in that it uses simplifications while being complete. Being complete means that we will find a path for a planning problem, whenever one exists.
Our algorithm QMP works by first decomposing the configuration into its quotient-spaces. Then we start growing a graph on the lowest-dimensional quotient-space until we find a feasible path. Once such a path has been found, we start growing a second graph on the next quotient-space. Both graphs are simultaneously grown until a feasible path is found on the next quotient-space. This process is continued until we find a path on the configuration space itself.
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| Research Progress Status |
平成30年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
平成30年度が最終年度であるため、記入しない。
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