[
    {
        "id": "osp-18165",
        "type": "article-journal",
        "title": "WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors",
        "author": [
            {
                "family": "Tang",
                "given": "Zhonghan"
            },
            {
                "family": "Li",
                "given": "Chenhui"
            },
            {
                "family": "Liang",
                "given": "Shuai"
            },
            {
                "family": "You",
                "given": "Zhongrui"
            },
            {
                "family": "Li",
                "given": "Jianan"
            },
            {
                "family": "Zhao",
                "given": "Bin"
            },
            {
                "family": "Wang",
                "given": "Zhigang"
            },
            {
                "family": "Li",
                "given": "Xuelong"
            }
        ],
        "URL": "https://omanscience.com/en/articles/wand-learning-robust-navigation-under-complex-wind-disturbances-and-dense-obstacles-for-quadrotors",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "DOI": "10.1109/lra.2026.3730214",
        "abstract": "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."
    }
]