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Online path planning for multiple unmanned aerial vehicle (multi-UAV) systemsis considered a challenging task. It needs to ensure collision-free pathplanning in real-time, especially when the multi-UAV systems can become verycrowded on certain occasions. In this paper, we presented a vision-baseddecentralized collision-avoidance policy for multi-UAV systems, which takesdepth images and inertial measurements as sensory inputs and outputs UAV'ssteering commands. The policy is trained together with the latentrepresentation of depth images using a policy gradient-based reinforcementlearning algorithm and autoencoder in the multi-UAV threedimensionalworkspaces. Each UAV follows the same trained policy and acts independently toreach the goal without colliding or communicating with other UAVs. We validateour policy in various simulated scenarios. The experimental results show thatour learned policy can guarantee fully autonomous collision-free navigation formulti-UAV in the three-dimensional workspaces with good robustness andscalability.

Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation