| Project/Area Number |
18K04045
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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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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | Yokohama National University |
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Principal Investigator |
Maeda Yusuke 横浜国立大学, 大学院工学研究院, 教授 (50313036)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | マニピュレータ / SLAM / 機構キャリブレーション / 地図作成 |
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| Outline of Final Research Achievements |
In this study, we proposed SKCLAM (Simultaneous Kinematic Calibration, Localization And Mapping) techniques, in which robot manipulators can perform environmental mapping and calibration of their kinematic parameters simultaneously. The original SLAM (Simultaneous Localization And Mapping) techniques for mobile robots were extended and applied to robot manipulators. In our proposed methods, a robot manipulator equipped with an RGB-D camera observes features in the environment or checker patterns to achieve the SKCLAM. Their effectiveness was demonstrated in experiments in virtual and actual environments.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究により，比較的安価なセンサを用いて，ロボットの機構キャリブレーションおよび環境地図作成を手軽に行うことが可能となった．この成果は，ロボットの動作精度の向上やロボットの動作自動生成につながるものであり，ものづくりの高度化・省力化に寄与するものと言える．
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