学习使用空中机器人集群在湍流中导航：一种协作式深度强化学习方法
Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach
AI总结：
本文提出基于GCNN的协作式深度强化学习多机器人控制器，通过邻居信息共享学习风流对团队的影响，实现可扩展且可泛化的湍流导航与补偿。
摘要：
由于流的混沌行为，在湍流环境中进行空中作业是一项具有挑战性的问题。当一组空中机器人试图在湍流风况下实现协调运动时，这一问题会变得更加复杂。本文提出了一种用于在湍流中导航的新型多机器人控制器，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，我们的方法并不学习在特定时间和空间补偿气流。相反，该方法学习根据流对机器人团队的影响来补偿流。这是通过一种深度强化学习方法实现的，该方法采用基于图卷积神经网络（GCNN）的架构，使机器人能够通过处理整个团队中风流的时空相关性来实现更好的风补偿。我们的方法可良好扩展到大型机器人团队——因为每个机器人仅使用来自其最近邻居的信息——并且能够很好地泛化到比训练中所见规模更大的机器人团队。仿真实验证明了信息共享如何改善空中机器人团队中的湍流补偿，并展示了我们的方法在不同团队配置上的灵活性。
英文摘要：
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.
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