  Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems
is considered a challenging task. It needs to ensure collision-free path
planning in real-time, especially when the multi-UAV systems can become very
crowded on certain occasions. In this paper, we presented a vision-based
decentralized collision-avoidance policy for multi-UAV systems, which takes
depth images and inertial measurements as sensory inputs and outputs UAV's
steering commands. The policy is trained together with the latent
representation of depth images using a policy gradient-based reinforcement
learning algorithm and autoencoder in the multi-UAV threedimensional
workspaces. Each UAV follows the same trained policy and acts independently to
reach the goal without colliding or communicating with other UAVs. We validate
our policy in various simulated scenarios. The experimental results show that
our learned policy can guarantee fully autonomous collision-free navigation for
multi-UAV in the three-dimensional workspaces with good robustness and
scalability.
