Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q network with monocular vision
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The fast developing of unmanned aerial vehicle(UAV) brings forward the higher request to the ability of autonomous obstacle avoidance in crowded environment. Small UAVs such as quadrotors usually integrate with simple sensors and computation units because of the limited payload and power supply, which adds difficulties for operating traditional obstacle avoidance method. In this paper, we present a framework to control a quadrotor to fly through crowded environments autonomously. This framework adopts a two-stage architecture, a sensing module based on unsupervised deep learning method and a decision module established on deep reinforcement learning method, which takes the monocular image as inputs and outputs quadrotor actions. And it enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets. To eliminate the negative effects of limited observation capacity of on-board monocular camera, the decision module uses dueling double deep recurrent Q networks. The trained model shows high success rate as it control a quadrotor to fly through crowded environments in simulation. And it can have good performance after scenario transformed.
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