Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have thus far fallen short of providing adequate 3D force-based models that can be incorporated into robust control paradigms required when designing and deploying dense flight formations. Thus, learning a model for these aerodynamic downwash patterns presents an attractive solution. However, given the computational cost and inadequacy of downwash field simulators for real-world flight settings, data collection for training is confined to real-world experimentation, enforcing the need for sample efficient methods. In this paper, we leverage the latent geometry (e.g., symmetries) present in the downwash fields to accurately and efficiently learn models for the experienced exogenic forces. Using real world experiments, we demonstrate that our geometry-aware model provides improvements over comparable baselines, even when the model is 1/35th the size and has access to a third of the training data.

翻译：多旋翼飞行器在近距离飞行时，会通过螺旋桨下洗流对彼此产生气动尾流效应。传统方法至今未能提供足够的三维基于力的模型，从而难以将其整合到设计和部署密集飞行编队所需的鲁棒控制范式中。因此，学习这些气动下洗流模式的模型是一个有吸引力的解决方案。然而，考虑到下洗流场模拟器在现实飞行环境中的计算成本和不适用性，用于训练的数据采集仅限于真实世界的实验，这迫使我们需要采用样本高效的方法。在本文中，我们利用下洗流场中存在的潜在几何结构（例如对称性），准确且高效地学习所受外力的模型。通过真实世界实验，我们证明即使模型大小仅为对比基线的1/35且训练数据仅为其三分之一，我们的几何感知模型仍能提供优于可比基线的改进效果。