Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
- URL: http://arxiv.org/abs/2607.12275v1
- Date: Tue, 14 Jul 2026 02:25:04 GMT
- Title: Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
- Abstract summary: Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled.<n>We propose a physics-informed residual dynamics learning framework that captures complex aerodynamic interactions while ensuring the joint multi-quadrotor system remains differentially flat.<n>We are the first to show that stable, tight formation flight can be achieved with under 30 seconds of training data and a 5ms loop rate, unlocking high-fidelity aerodynamic compensation for compute-constrained flight stacks.
- Score: 12.401790739166222
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quadrotors flying in tight formations are severely affected by turbulent aerodynamic interactions, such as downwash, that can cause catastrophic collisions if left unmodeled. To compensate for these effects, we propose a physics-informed residual dynamics learning framework that captures complex aerodynamic interactions while ensuring the joint multi-quadrotor system remains differentially flat. We leverage this preserved flatness to design a computationally efficient feedback linearization controller that is easily tunable with linear control techniques and cancels aerodynamic disturbances via feedforward compensation. Hardware experiments demonstrate our framework reduces average tracking errors by 31% compared to nominal baselines. Crucially, our lightweight approach matches the tracking performance of state-of-the-art nonlinear model predictive control (NMPC) while requiring an order of magnitude less computation. We are the first to show that stable, tight formation flight can be achieved with under 30 seconds of training data and a 5ms loop rate, unlocking high-fidelity aerodynamic compensation for compute-constrained flight stacks.
Related papers
- A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
arXiv Detail & Related papers (2026-08-11T01:51:08Z) - Rollback-Free Stable Brick Structures Generation [51.856925926010184]
Existing approaches rely on external physical simulators during inference to perform rejection sampling and brick-by-brick rollbacks.<n>We propose a reinforcement learning paradigm that shifts physical validity enforcement from test-time correction to training-time policy optimization.<n> Experimental results demonstrate that our approach achieves state-of-the-art generation quality while accelerating inference speed by orders of magnitude.
arXiv Detail & Related papers (2026-05-07T21:06:44Z) - Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
arXiv Detail & Related papers (2026-04-10T17:12:07Z) - Physics-infused Learning for Aerial Manipulator in Winds and Near-Wall Environments [3.895172208362693]
Aerial manipulation (AM) expands UAV capabilities beyond passive observation to contact-based operations at high altitudes and in otherwise inaccessible environments.<n>This paper integrates a physics-based blade-element model with a learning-based residual force estimator, along with a rotor-speed allocation strategy for disturbance compensation, resulting in a unified control framework.<n>Results demonstrate improved disturbance estimation and trajectory-tracking accuracy over conventional approaches, enabling robust wall-contact execution under challenging aerodynamic conditions.
arXiv Detail & Related papers (2026-03-08T22:08:15Z) - Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight [25.084985306310827]
This paper introduces a unified time-optimal trajectory optimization framework for vision-based quadrotors.<n>It incorporates perception constraints alongside full nonlinear dynamics, rotor actuation limits, aerodynamic effects, camera field-of-view constraints, and convex geometric gate representations.<n>Experiments demonstrate real-world flight speeds up to 9.8 m/s with 0.07 m average tracking error, and closed-loop success rates improved from 55% to 100%.
arXiv Detail & Related papers (2026-03-04T17:22:16Z) - 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) - Online Adaptation for Flying Quadrotors in Tight Formations [7.445987710491257]
Complex aerodynamic wake interactions can destabilize individual team members as well as the team.<n>We present L1 KNODE-DW MPC, an adaptive, mixed expert learning based control framework.<n>Our results show that the proposed framework is capable of enabling the three-quadrotor team to remain vertically aligned in close proximity throughout the flight.
arXiv Detail & Related papers (2025-06-20T21:49:17Z) - Flying Quadrotors in Tight Formations using Learning-based Model Predictive Control [30.715469693232492]
In this work, we propose a framework that combines the benefits of first-principles modeling and data-driven approaches.
We show that incorporating the model into a novel learning-based predictive model control framework results in substantial performance improvements.
Our framework also achieves exceptional sample efficiency, using only a total of 46 seconds of flight data for training.
arXiv Detail & Related papers (2024-10-13T05:03:16Z) - Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning [10.191783697332227]
Machine learning models provide a promising way to rapidly acquire transonic swept wing flow fields.
We propose a physics-embedded transfer learning framework to efficiently train the model.
When reducing the dataset size, less than half of the wing training samples are need to reach the same error level as the nontransfer framework.
arXiv Detail & Related papers (2024-09-19T12:35:59Z) - Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate
Model Predictive Trajectory Tracking [76.27433308688592]
Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation.
We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience.
Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
arXiv Detail & Related papers (2022-06-07T13:51:35Z) - 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)
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.