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

• URL: http://arxiv.org/abs/2507.15444v1
• Date: Mon, 21 Jul 2025 09:53:42 GMT
• Title: Low-Latency Event-Based Velocimetry for Quadrotor Control in a Narrow Pipe
• Authors: Leonard Bauersfeld, Davide Scaramuzza,
• Abstract summary: In this work, we present the first closed-loop control system for quadrotors for hovering in narrow pipes.<n>We develop a low-latency, event-based smoke velocimetry method that estimates local airflow at high temporal resolution.<n>The flow-feedback control proves particularly effective during lateral translation maneuvers in the pipe cross-section.
• Score: 22.30914818152441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows [109.99020160824553]
  We propose an efficient deep reinforcement learning framework for modeling and control of turbulent flows.<n>It is model-based RL for predictive control (PC), where both the policy and the observer models for turbulence control are learned jointly.<n>We find that PINO-PC achieves a drag reduction of 39.0% under a bulk-velocity Reynolds number of 15,000, outperforming previous fluid control methods by more than 32%.
  arXiv  Detail & Related papers  (2025-10-03T00:18:26Z)
• Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction [6.508732875368554]
  We show that combining operator learning with generative modeling overcomes this limitation.<n>For turbulence 3D homogeneous isotropic, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times.<n>For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative phase infers full 3D velocity and pressure fields with correct statistics.
  arXiv  Detail & Related papers  (2025-09-10T16:42:22Z)
• Improving Action Smoothness for a Cascaded Online Learning Flight Control System [7.871518182413388]
  We introduce an online temporal smoothness technique and a low-pass filter to reduce the amplitude and frequency of the control actions.<n> Simulation results demonstrate the improvements achieved by the two proposed techniques.
  arXiv  Detail & Related papers  (2025-07-06T11:19:34Z)
• Physics-informed neural networks for hidden boundary detection and flow field reconstruction [0.0]
  This study presents a physics-informed neural network (PINN) framework designed to infer the presence, shape, and motion of static or moving solid boundaries.<n>The framework is validated across diverse scenarios, including incompressible Navier-Stokes and compressible Euler flows.<n>The proposed method exhibits robustness and versatility, highlighting its potential for applications when only limited experimental or numerical data are available.
  arXiv  Detail & Related papers  (2025-03-31T13:30:46Z)
• AccFlow: Backward Accumulation for Long-Range Optical Flow [70.4251045372285]
  This paper proposes a novel recurrent framework called AccFlow for long-range optical flow estimation. We demonstrate the superiority of backward accumulation over conventional forward accumulation. Experiments validate the effectiveness of AccFlow in handling long-range optical flow estimation.
  arXiv  Detail & Related papers  (2023-08-25T01:51:26Z)
• Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference [73.63024765499719]
  Mobile networks can be compromised by interference from neighboring cells or jammers. We propose Spectrum Breathing, which cascades-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. We show a performance tradeoff between gradient-pruning and interference-induced error as regulated by the breathing depth.
  arXiv  Detail & Related papers  (2023-05-10T07:05:43Z)
• Forecasting subcritical cylinder wakes with Fourier Neural Operators [58.68996255635669]
  We apply a state-of-the-art operator learning technique to forecast the temporal evolution of experimentally measured velocity fields. We find that FNOs are capable of accurately predicting the evolution of experimental velocity fields throughout the range of Reynolds numbers tested.
  arXiv  Detail & Related papers  (2023-01-19T20:04:36Z)
• Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems [0.8411385346896411]
  We present a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforcement Learning estimator for unknown aerodynamic effects into a Model Predictive Controller. We demonstrate our system to improve the cumulative tracking errors by at least 66% with unknown and diverse aerodynamic forces.
  arXiv  Detail & Related papers  (2022-05-14T23:27:38Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Network [0.0]
  Figuring out the right airfoil is a crucial step in the preliminary stage of any aerial vehicle design. This study compares different network architectures and training datasets in an attempt to gain insight as to how the network perceives the given airfoil geometries.
  arXiv  Detail & Related papers  (2021-09-24T19:07:19Z)
• T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting [65.498967509424]
  Air turbulence forecasting can help airlines avoid hazardous turbulence, guide routes that keep passengers safe, maximize efficiency, reduce costs. Traditional forecasting approaches rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. We propose a machine learning based turbulence forecasting system due to two challenges: (1) Complex-temporal correlations, and (2) scarcity, very limited turbulence labels can be obtained.
  arXiv  Detail & Related papers  (2020-10-26T21:14:15Z)
• Transition control of a tail-sitter UAV using recurrent neural networks [80.91076033926224]
  The control strategy is based on attitude and velocity stabilization. The RNN is used for the estimation of high nonlinear aerodynamic terms. Results show convergence of linear velocities and the pitch angle during the transition maneuver.
  arXiv  Detail & Related papers  (2020-06-29T21:33:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.