Low-Latency Event-Based Velocimetry for Quadrotor Control in a Narrow Pipe

2025-07-21

AI Summary

Quadrotors operating in narrow pipes suffer from instability and wall collisions due to self-induced aerodynamic disturbances—especially during hover and lateral translation—where conventional sensing and control fail under severe flow confinement. Method: We propose the first real-time closed-loop solution integrating flow-field perception and control: an event-based camera enables low-latency smoke velocimetry; a recurrent convolutional neural network (RCNN) estimates local aerodynamic disturbances online; and a reinforcement learning–based controller actively compensates for these disturbances. Contribution/Results: This work pioneers the direct incorporation of high spatiotemporal-resolution flow measurements into quadrotor closed-loop control. Experiments demonstrate stable hover and controlled lateral maneuvering inside narrow cylindrical pipes. The approach reveals canonical flow structures in confined spaces, significantly enhances disturbance rejection, and eliminates wall collisions—establishing a new paradigm for autonomous aerial robot navigation in complex, aerodynamically challenging environments.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• Autonomous quadrotor hovering in narrow pipes with aerodynamic disturbances
• Real-time flow field measurement for closed-loop control
• Counteracting transient aerodynamic effects during lateral maneuvers

Innovation

• Event-based smoke velocimetry for airflow estimation
• Recurrent CNN for real-time disturbance estimation
• Reinforcement learning-based controller with flow feedback