发表机构
University of Science and Technology of China; Shanghai Artificial Intelligence Laboratory（中国科学技术大学; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出WAND强化学习框架，通过TCN估计风扰动加速度并结合WindAdapter实现扰动抑制，在12种仿真场景中成功率平均提升8.3个百分点，20次室内飞行试验成功18次，验证了四旋翼无人机在复杂环境下的鲁棒导航能力。
AI 中文摘要
在杂乱环境中的鲁棒导航仍是四旋翼无人机面临的核心挑战，尤其当强风扰动出现时，会干扰飞行器动力学、限制控制权限并大幅提升碰撞风险。现有基于学习的导航策略通常依赖障碍物感知与本体感知观测，要求策略隐式推断时变扰动效应，从而限制了部分可观测性下的鲁棒性。本文提出WAND（Wind-Aware Navigation with Disturbance Estimation，即感知扰动估计的风感知导航），这是一种用于在密集障碍物场中应对时变风扰动的强化学习框架。具体而言，WAND使用时间卷积网络（TCN）从历史本体感知状态中估计风诱导的扰动加速度，该估计通过零初始化的残差模块WindAdapter集成到策略中，同时为低级控制提供前馈补偿。这种对估计值的双重使用，将扰动条件导航与前馈扰动抑制相结合。在12种受风扰动的仿真设置中，WAND相比仅使用前馈补偿的方法，观测到的成功率平均提升了8.3个百分点；受控的逆侧风实验进一步显示了依赖风向的轨迹适应性；在室内风扇诱导的飞行测试中，WAND在20次试验中成功完成18次，证明了实时机载导航的可行性。
英文摘要
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.
Journal refIEEE Robotics and Automation Letters, vol. 11, no. 11, pp. 12392-12399, Nov. 2026
