| 研究課題/領域番号 |
23K19096
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| 研究種目 |
研究活動スタート支援
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| 配分区分 | 基金 |
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| 審査区分 |
0301:材料力学、生産工学、設計工学、流体工学、熱工学、機械力学、ロボティクス、航空宇宙工学、船舶海洋工学およびその関連分野
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| 研究機関 | 北陸先端科学技術大学院大学 |
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研究代表者 |
NGUYEN HuuNhan 北陸先端科学技術大学院大学, 先端科学技術研究科, 助教 (80981159)
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| 研究期間 (年度) |
2023-08-31 – 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 |
2,860千円 (直接経費: 2,200千円、間接経費: 660千円)
2024年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2023年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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| キーワード | Simulation framework / Dynamic behaviors / Uncertainties / Reinforcement Learning / Soft Robotics / Adaptable Morphology / Embodied Intelligence |
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| 研究開始時の研究の概要 |
This research proposal introduces a novel approach for developing a class of soft robots that can actively self-configure their body (i.e., morphology) to allow adaptation with respect to internal and external varied factors. The core idea is to integrate proprioceptive feedback driven by morphology-dependent features (geometrical/material properties) into the control loop. To achieve this, a platform of multi-physics simulation and learning-based techniques seeking for optimal morphology transformation strategy to accommodate various critical conditions will be established.
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| 研究実績の概要 |
The achievements of this fiscal year primarily revolve around the first phase (WP1), which is dedicated to developing a simulation framework. This framework aims to accomplish two main objectives:
1) Emulate the dynamic behaviors of a soft body under the effects of external and internal physical stimulation, and 2) Introduce physical uncertainties, such as cutting, and track the corresponding mechanical responses (strain) under these conditions.
Note that physical damages can be simulated either offline, using external tools for remeshing, or online, where remeshing is conducted within the environment itself. Additionally, we have successfully interfaced this simulation tool with a reinforcement learning environment and have validated with various algorithms.
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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
Based on the results presented above, we are confident that the first Work Package (WP1) has nearly achieved its objectives as anticipated. However, a limitation remains: the current framework only supports the exploitation of a single robot at a time. In practice, we need to be able to handle a large number of potential solutions, encompassing various robot designs and control strategies. Therefore, it is imperative to enable parallel sampling and training across the entire design space. This capability is crucial for efficiently exploring not only suitable control policies but also optimal morphologies for adaptive behaviors.
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| 今後の研究の推進方策 |
The primary objective is to enhance the aforementioned framework by enabling it to simultaneously call multiple environments, each corresponding to a different robot design, as sub-processes. Additionally, an interface will be developed to assign design parameters to each environment.
Next, this comprehensive framework will be leveraged to conduct a joint search for the optimal morphology, including its dynamic transition, and control policy. This search will address pre-defined challenges such as unexpected physical damage, environmental changes, and the need to enhance the robot's functionality (e.g., sensing capabilities). The outcome will be evaluated via an experimental study.
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