Accurate aerodynamic interaction modeling in multi-drone tasks is crucial for enhancing system stability and efficiency, especially when facing major disturbances from downwash wake effects. Conventional data-driven and empirical models mainly address simplified cases where one drone hovers or all vehicles have low absolute and relative velocities (≤ 0.5 m/s), and rely merely on relative states. In this study, we use high-fidelity Computational Fluid Dynamics (CFD) simulations to explore quadrotor interactions at higher speeds (0.5-4.0 m/s). We find that as the absolute velocities of the UAVs rise, downwash effects change significantly. To account for these discrepancies, we present a data-driven model considering both the absolute and relative properties of the downwash problem. We propose a geometric deep neural network predictor and compare its performance with existing data-driven and empirical models. Validations on two quadrotor settings show that our model gives more reliable predictions in tough scenarios and performs better in training without rigorous fine-tuning. Finally, we combine our predictor with a nonlinear feedback controller to enhance flight control under downwash disturbances. However, we encounter limitations for our speed ranges during trajectory tracking such as delays and velocity loss. Despite these challenges, our encoding and prediction method shows to be a promising step to address the downwash effects at higher speeds.We release our dataset, method, and re-implementations at: https://github.com/pavelkharitenko/flare-dw.

Unmanned aerial vehicles (UAVs) have demonstrated remarkable efficacy across diverse fields. Nevertheless, developing flight controllers tailored to a specific UAV design, particularly in environments with strong fluid-interactive dynamics, remains challenging. Conventional controller design experiences often fall short in such cases, rendering it infeasible to apply time-tested practices. Consequently, a simulation test bed becomes indispensable for controller design and evaluation prior to its actual implementation on the physical UAV. This platform should allow for meticulous adjustment of controllers and should be able to transfer to real-world systems without significant performance degradation. Existing simulators predominantly hinge on empirical models due to high efficiency, often overlooking the dynamic interplay between the UAV and the surrounding airflow. This makes it difficult to mimic more complex flight maneuvers, such as an abrupt midair halt inside narrow channels, in which the UAV may experience strong fluid-structure interactions. On the other hand, simulators considering the complex surrounding airflow are extremely slow and inadequate to support the design and evaluation of flight controllers. In this paper, we present a novel remedy for highly-efficient UAV flight simulations, which entails a hybrid modeling that deftly combines our novel far-field adaptive block-based fluid simulator with parametric empirical models situated near the boundary of the UAV, with the model parameters automatically calibrated. With this newly devised simulator, a broader spectrum of flight scenarios can be explored for controller design and assessment, encompassing those influenced by potent close-proximity effects, or situations where multiple UAVs operate in close quarters. The practical worth of our simulator has been authenticated through comparisons with actual UAV flight data. We further showcase its utility in designing flight controllers for fixed-wing, multi-rotor, and hybrid UAVs, and even exemplify its application when multiple UAVs are involved, underlining the unique value of our system for flight controllers.

Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

We present Neural Structured Learning (NSL) in TensorFlow [1], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow [2] ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.