| 研究課題/領域番号 |
20K14706
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分20020:ロボティクスおよび知能機械システム関連
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| 研究機関 | 国立天文台 |
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研究代表者 |
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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| 研究期間 (年度) |
2020-04-01 – 2025-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
2023年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
2022年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2021年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
2020年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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| キーワード | machine learning / computer vision / deep learning / numerical simulation / orbital dynamics / astronomy / interferometry / space exploration / autonomous vehicles |
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| 研究開始時の研究の概要 |
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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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
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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| 今後の研究の推進方策 |
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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