| Project/Area Number |
20K14706
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | National Astronomical Observatory of Japan |
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Principal Investigator |
Wu Benjamin 国立天文台, アルマプロジェクト, 特別客員研究員 (50868718)
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| Project Period (FY) |
2020-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2023: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2022: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2021: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2020: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | machine learning / computer vision / deep learning / numerical simulation / orbital dynamics / astronomy / interferometry / space exploration / autonomous vehicles |
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| Outline of Research at the Start |
For planetary rovers, localization (determining the precise location) is a fundamental task for exploration and science. This project uses machine learning and computer vision to improve localization accuracy and autonomy over current methods.
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| Outline of Annual Research Achievements |
We are continuing to apply machine learning to problems in space research and exploration and establish deep learning computational hardware capabilities at NAOJ.
In FY2023, scientists from NASA Ames initiated bi-weekly collaboration meetings regarding orbital debris remediation, largely based on techniques from our FY2022 paper "Low-thrust rendezvous trajectory generation for multi-target active space debris removal using the RQ-Law".
We also continued to develop the computer vision based methods for lunar rover absolute localization.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The original research topic of rover localization progressed slower than planned. However, this is due to several new research directions emerging, which have been very fruitful with multiple publications and collaborations.
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| Strategy for Future Research Activity |
For rover localization, we are exploring the use of vision transformers compared to the previous ResNet baseline. Due high interest in our work on orbital dynamics, we have pursued research collaborations in this direction. We are extending this work by scaling up the number of debris objects that can be targeted for deorbit.
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