Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in turbulent flows, decoupling the trajectory-tracking control from the turbulence compensation via a nested control architecture. Unlike previous works, our method does not learn to compensate for the air-flow at a specific time and space. Instead, our method learns to compensate for the flow based on its effect on the team. This is made possible via a deep reinforcement learning approach, implemented via a Graph Convolutional Neural Network (GCNN)-based architecture, which enables robots to achieve better wind compensation by processing the spatial-temporal correlation of wind flows across the team. Our approach scales well to large robot teams -- as each robot only uses information from its nearest neighbors -- , and generalizes well to robot teams larger than seen in training. Simulated experiments demonstrate how information sharing improves turbulence compensation in a team of aerial robots and demonstrate the flexibility of our method over different team configurations.

翻译：在湍流环境中执行空中作业是一项极具挑战性的问题，原因在于流体的混沌行为。当一组空中机器人试图在湍流风况下实现协同运动时，这一问题将变得更加复杂。本文提出了一种新颖的多机器人控制器，用于在湍流中导航，通过嵌套控制架构将轨迹跟踪控制与湍流补偿解耦。与以往工作不同，我们的方法并非学习在特定时空点补偿气流，而是基于气流对整个机器人群组的影响来学习补偿。这一目标通过基于图卷积神经网络（GCNN）架构的深度强化学习方法实现，使机器人能够通过处理整个群组中气流的时空相关性，获得更优的风补偿性能。我们的方法可良好扩展至大规模机器人集群——因为每个机器人仅需使用其最近邻居的信息——并能泛化至比训练时规模更大的机器人集群。仿真实验证明了信息共享如何提升空中机器人集群的湍流补偿能力，并展示了我们的方法在不同集群配置下的灵活性。