Jialong Chen

As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, EventPro. EventPro features two components: Count Every Rotation achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. Every Rotation Counts leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that EventPro achieves a sensing latency of 3 ms and a rotational speed estimation error of merely 0.23%. Additionally, EventPro infers drone flight commands with 96.5% precision and improves drone tracking accuracy by over 22% when combined with other sensing modalities. Demo: https://eventpro25.github.io/EventPro/.

This study presents an aerodynamic characterization of a fan-array wind generator (FAWG) operating at Reynolds number of 2.8×106 in a large room. The performance of unmanned aerial vehicles needs to be tested for a wide range of spatiotemporal wind conditions. For this purpose, we have built a FAWG with 40 × 40 individually controllable fans, covering a wind section of 3.25 m × 3.25 m with speeds up to 13 m/s. This wind section is monitored with an equidistant 8 × 8 pitot tube array and a hot-wire anemometer for different levels of uniform blowing. The flow has a nearly uniform core region, which shrinks similar to the potential core of a jet. Similar observations have been presented for a small FAWG [Li et al., “Aerodynamic characterization of a fan-array wind generator,” AIAA J. 62(1), 291–301 (2024)]. The wind velocity in the flow direction is observed to increase linearly with the duty cycle of the pulse width modulation signal employed to command the fan rotation speed. We demonstrate one key advantage of FAWG: it can modulate the wind velocity within 2 s.

We propose an analytical rotor force model for multicopter drones and derive corresponding performances for endurance, climb, and cruise. Multicopter drones follow their planned trajectory by controlling the angular velocities of the rotors. This motion is described by an aerodynamic drone model comprising a distributed mass and the rotor forces. Previous rotor force models focus on hovering and slow-speed motion, i.e., when the thrust is nearly perpendicular to the plane. In the current study, we generalize these rotor force models for high-speed cruise and large ambient flow velocities. These generalized models allow to predict and optimize drone performance for hovering, climbing, and cruising. Key enablers are a large database for rotor performance under different ambient winds and a novel sparse regression for the normal and in-plane rotor forces. The modeling and optimization framework is exemplified for the rotor of a lightweight quadcopter drone (800 grams) and has an angular velocity range of 70–150 revolutions per second (RPS). Different angular velocities, incidence angles, and flight velocities are considered. The maximum incoming velocity with different directions is set as 15 m/s to avoid flow separation. The presented framework can guide drone performance optimization and gust-safe model-based control.

We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various working conditions, thus allowing the extrapolation for wind conditions far beyond the training data. Another key enabler is a machine-learned non-dimensionalized manifold from snapshot data. The velocity field is modeled using a double encoder–decoder approach. The first encoder normalizes data using the oncoming wind speed, while the second encoder projects this normalized data onto the isometric feature mapping manifold. The decoders reverse this process, with k-nearest neighbor performing the first decoding and the second undoing the normalization. The manifold is coarse-grained by clustering to reduce the computational load for de- and encoding. The sensor-based flow estimation is based on the estimate of the oncoming wind speed and a mapping from sensor signal to the manifold latent variables. The proposed machine-learned flow estimation framework is exemplified for the flow above an unmanned aerial vehicle vertiport. The wind estimation is shown to generalize well for rare wind conditions, not included in the original database.