The Structure of Merging Turbulent Jets Beneath a Small Quadrotor

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.

Small rotorcraft intended for use indoors or around the built environment have extremely limited flight duration. This paper presents the design and experimental characterization of a custom shroud system that transforms a Crazyflie 2.1 micro air vehicle into a multi-modal robot capable of operating as a high-efficiency hovercraft or a free-flying drone. A custom experimental platform was developed for precise control of hover height and rotor duty cycle, and automated data logging of lift forces. Parametric testing of duct, intake, and nozzle geometries was performed to investigate the impact of shroud configuration on in-ground-effect and free-flight performance. An empirical model is developed which, unlike typical models for ground effect in rotorcraft, captures the suckdown effect that reduces force at intermediate height. It is shown that, through proper design of the shroud, beneficial ground effects can be increased while diminishing negative effects both close to the ground and in free flight. An optimized configuration exhibited nearly three times higher in-ground-effect force while maintaining comparable out-of-ground-effect aerodynamic thrust, although the added shroud mass reduces free-flight control authority. Lightweight shrouds are manufactured using thin-film thermoformed components, and total single-charge flight time is shown to increase by 60% in-ground-effect while decreasing by only 30% in free-flight as compared to the stock drone. Finally, controlled flight in the air, hovering close to the ground, and hover-to-flight transitions are demonstrated using a simple mode-switching controller, with tracking errors reported to quantify performance. This work provides an experimentally-validated and easily adoptable foundation for future research into lightweight ground-effect vehicles and hybrid drone-hovercraft systems.

Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control. This work illustrates a two-stage learning pipeline that first estimates the local wind from onboard kinematics and dynamics and then exploits that estimate inside a reinforcement learning (RL) flight controller. The wind estimator, an attention-augmented gated recurrent network trained on thousands of simulated flights through von Karman turbulence with power-law shear and veer, recovers the horizontal wind vector with a per-flight root-mean-square error of 0.40 m/s and a direction error of 3.2 degrees on unseen wind regimes, an accuracy near the floor imposed by unresolved turbulence, and generalizes to vertical ascent profiles with a skill score of 0.861 over a constant-wind reference. A proximal policy optimization controller receiving the frozen estimator's output reduces horizontal trajectory tracking error by 48% relative to a wind-blind proportional-derivative baseline across mean winds of 4 m/s to 12 m/s, winning on 100% of evaluation episodes. A three-way ablation decomposes this improvement into a kinematic component, available without wind information, and a wind-perception component; the perception share rises with wind speed, from small in light winds toward roughly half the total benefit in strong winds, consistent with the quadratic scaling of aerodynamic drag. The controller degrades gracefully on out-of-distribution winds of 13 m/s to 15 m/s, where the baseline fails catastrophically.