強化学習による制御と,硬直リンクロボット魚の設計: 総合的なレビュー
Nhat Dinh1, Darion Vosbein1, Yuehua Wang2
1Smart Devices and Intelligent Systems Laboratory, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
自律型ロボット魚,特にリジッドリンク魚ロボット (RLFR) は,効率的な水中探査を提供します. このレビューは,それらの構造設計と強化学習制御の詳細を述べ,将来の開発のための進歩と課題を強調しています.
科学分野:
- ロボット工学 ロボット工学 ロボット工学
- マリンエンジニアリングは,海洋工学です.
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- 自律型ロボット魚は,海洋調査,インフラストラクチャの検査,環境モニタリングにおいてますます重要になっています.
- 魚の脊髄にインスパイアされたリジードリンクフィッシュロボット (RLFR) は,バイオミメティックな推進力,機敏な動き,効率的な水中操作を提供します.
- モデルの設計により,費用対効果が高く,簡単に組み立てられ,複雑な水中の環境にも適しています.
研究 の 目的:
- 固いリンク魚ロボット (RLFR) の構造設計における最近の進歩をレビューする.
- センサーとアクチュエータを含むRLFR制御システムの強化学習 (RL) の適用を検証する.
- RLFR技術における現在の技術的ギャップと将来の研究方向を特定する.
主な方法:
- 関節構成に基づく既存のRLFR設計の分類.
- 構造設計における重要な考慮事項の要約:材料,製造,推進.
- RLFR制御に適用される強化学習アルゴリズム (Q-learning, DQN, DDPG) の分析.
主要な成果:
- RLFRは,波動的な動きを通じて高い操縦能力と効率的な移動を証明しています.
- 強化学習アルゴリズムは,ダイナミックな水力動力学的条件下でRLFRの適応性と運動制御を強化します.
- RLFRの性能に影響を与える主要な構造,材料,製造,および推進要因が特定されています.
結論:
- 固いリンクの魚ロボット (RLFR) は,自律的な水中システムにおける重要な進歩を表しています.
- 強化学習は,複雑な環境でRLFRを制御するための強力な能力を提供します.
- 構造化されていない環境や流体体相互作用における課題に対処するために,RLFRの性能を向上させるため,さらなる研究が必要である.
キーワード:
Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learning Q-learningディープQネットワーク深い決定論的な政策のグラデントグラデント強化学習による学習です.厳格なリンク 魚 ロボット
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