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The rapid development of unmanned aerial vehicles (UAV) puts forward a higherrequirement for autonomous obstacle avoidance. Due to the limited payload andpower supply, small UAVs such as quadrotors usually carry simple sensors andcomputation units, which makes traditional methods more challenging toimplement. In this paper, a novel framework is demonstrated to control aquadrotor flying through crowded environments autonomously with monocularvision. The framework adopts a two-stage architecture, consisting of a sensingmodule and a decision module. The sensing module is based on an unsuperviseddeep learning method. And the decision module uses dueling double deeprecurrent Q-learning to eliminate the adverse effects of limited observationcapacity of an on-board monocular camera. The framework enables the quadrotorto realize autonomous obstacle avoidance without any prior environmentinformation or labeled datasets for training. The trained model shows a highsuccess rate in the simulation and a good generalization ability fortransformed scenarios.

Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q-learning with monocular vision