Autonomous quadrotor flight in confined spaces such as pipes and tunnels presents significant challenges due to unsteady, self-induced aerodynamic disturbances. Very recent advances have enabled flight in such conditions, but they either rely on constant motion through the pipe to mitigate airflow recirculation effects or suffer from limited stability during hovering. In this work, we present the first closed-loop control system for quadrotors for hovering in narrow pipes that leverages real-time flow field measurements. We develop a low-latency, event-based smoke velocimetry method that estimates local airflow at high temporal resolution. This flow information is used by a disturbance estimator based on a recurrent convolutional neural network, which infers force and torque disturbances in real time. The estimated disturbances are integrated into a learning-based controller trained via reinforcement learning. The flow-feedback control proves particularly effective during lateral translation maneuvers in the pipe cross-section. There, the real-time disturbance information enables the controller to effectively counteract transient aerodynamic effects, thereby preventing collisions with the pipe wall. To the best of our knowledge, this work represents the first demonstration of an aerial robot with closed-loop control informed by real-time flow field measurements. This opens new directions for research on flight in aerodynamically complex environments. In addition, our work also sheds light on the characteristic flow structures that emerge during flight in narrow, circular pipes, providing new insights at the intersection of robotics and fluid dynamics.

翻译：在管道和隧道等受限空间内实现四旋翼飞行器的自主飞行面临重大挑战，这主要源于非定常、自诱导的空气动力扰动。最新进展虽已实现在此类条件下的飞行，但现有方法要么依赖管道内的持续运动以减弱气流再循环效应，要么在悬停时稳定性受限。本研究首次提出了一种利用实时流场测量的四旋翼飞行器狭窄管道悬停闭环控制系统。我们开发了一种低延迟、基于事件的烟雾测速方法，能够以高时间分辨率估计局部气流。该流动信息由一个基于循环卷积神经网络的扰动估计器所利用，该网络可实时推断力与力矩扰动。估计出的扰动被整合到一个通过强化学习训练得到的基于学习的控制器中。流场反馈控制在管道横截面内的横向平移机动中表现出显著优势。实时扰动信息使控制器能有效抵消瞬态空气动力效应，从而避免与管壁发生碰撞。据我们所知，本研究首次展示了基于实时流场测量实现闭环控制的空中机器人系统。这为在空气动力学复杂环境中的飞行研究开辟了新方向。此外，我们的工作还揭示了在狭窄圆形管道内飞行时出现的特征性流动结构，为机器人学与流体动力学的交叉领域提供了新的见解。