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This research presents WAND, a reinforcement learning framework that enhances quadrotor navigation by explicitly estimating and compensating for time-varying wind disturbances using Temporal Convolutional Networks. By integrating disturbance estimation into the policy via a WindAdapter module, the system achieves significantly higher success rates in cluttered environments compared to standard feedforward methods. The study validates real-time onboard feasibility through both simulation and indoor fan-induced flight tests, addressing a critical gap in autonomous aerial robotics under partial observability.
WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors
arXiv:2610.11809v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2610.11809 [cs.RO]
(or arXiv:2610.11809v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.11809 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
IEEE Robotics and Automation Letters, vol. 11, no. 11, pp. 12392-12399, Nov. 2026
https://doi.org/10.1109/LRA.2026.3730214 Focus to learn more DOI(s) linking to related resources
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