荒れた海面への視覚に基づく機敏な着陸
Vision-Based Agile Landing on Turbulent Waters
強化学習を用いて、移動する海上プラットフォームへのマルチローターの自律着陸を、プラットフォームの状態推定なしで視覚特徴のみから実現する手法を提案した。
詳しい要約
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著者: Dimosthenis Angelis, Leonard Bauersfeld, Davide Scaramuzza, Evangelos Boukas
分類: cs.RO
原文アブストラクト
Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-sea conditions. This paper presents a reinforcement-learning-based approach for autonomous multirotor landing on moving maritime platforms without requiring explicit platform-state observations or estimation during deployment. The proposed method uses multirotor state measurements together with local visual features, consisting of keypoints and associated descriptors extracted from the landing surface, to predict attitude and thrust commands. These commands are tracked by a conventional low-level controller. The policy is trained in simulation using synthetic keypoints with randomly generated normalized descriptors, enabling zero-shot deployment with different local feature extractors onboard the UAV. We evaluate the method in a realistic simulator and show that it outperforms a state-of-the-art Model Predictive Control baseline under platform motions corresponding to ''Very Rough'' sea conditions. Finally, we perform extensive real-world experiments, demonstrating autonomous onboard landing using two different local feature extractors. To the best of our knowledge, this is the first approach for agile multirotor landing on maritime platforms in turbulent waters that does not rely on an explicit platform-state during deployment.
関連論文
- 強化学習による移動プラットフォームへの自律マルチロータ着陸強化学習/着陸
- UAVの視覚ベース着艦のためのロバスト強化学習アルゴリズム強化学習/着陸
- 深層強化学習によるクアッドロータの自律着陸強化学習/着陸
