Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A
  Cooperative Deep Reinforcement Learning Approach
Diego Patiño Siddharth Mayya Juan Calderon Kostas Daniilidis David Saldaña
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.