WAND: 複雑な風外乱と密集障害物下での四 rotor のロバストナビゲーション学習
WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors
履歴的な固有受容状態から風外乱加速度を推定し、強化学習ポリシーとフィードフォワード制御に統合することで、密集障害物環境での四 rotor のロバストナビゲーションを実現した。
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著者: Zhonghan Tang, Chenhui Li, Shuai Liang, Zhongrui You, Jianan Li, Bin Zhao, Zhigang Wang, Xuelong Li
分類: cs.RO
原文アブストラクト
Robust navigation in cluttered environments remains a fundamental challenge for quadrotors, particularly when strong wind disturbances arise, which perturb vehicle dynamics, limit control authority, and substantially increase collision risk. Existing learning-based navigation policies typically rely on obstacle perception and proprioceptive observations, requiring the policy to infer time-varying disturbance effects implicitly and thereby limiting robustness under partial observability. This paper proposes WAND (Wind-Aware Navigation with Disturbance Estimation), a reinforcement learning framework for navigation under time-varying wind disturbances in dense obstacle fields. Specifically, WAND estimates wind-induced disturbance acceleration from historical proprioceptive states using a Temporal Convolutional Network (TCN). This estimation is integrated into the policy via a zero-initialized residual module, \emph{WindAdapter}, while simultaneously providing feedforward compensation for low-level control. The dual use of the estimate couples disturbance-conditioned navigation with feedforward disturbance rejection. Across 12 wind-disturbed simulation settings, WAND improved the observed success rate by 8.3 percentage points on average relative to feedforward compensation alone. Controlled opposite-crosswind experiments further showed wind-direction-dependent trajectory adaptation. In indoor fan-induced flight tests, WAND succeeded in 18 of 20 trials, demonstrating the feasibility of real-time onboard navigation.