Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach
This addresses the challenge of robust multi-robot navigation in chaotic wind environments for applications like search and rescue or environmental monitoring, representing an incremental advance in swarm control methods.
The paper tackles the problem of coordinating aerial robot swarms in turbulent flows by introducing a cooperative deep reinforcement learning controller that decouples trajectory tracking from turbulence compensation, enabling robots to compensate based on team effects rather than specific flow conditions, with simulated experiments showing improved compensation and scalability to larger teams.
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.