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In this paper, we propose a map-based end-to-end DRL approach forthree-dimensional (3D) obstacle avoidance in a partially observed environment,which is applied to achieve autonomous navigation for an indoor mobile robotusing a depth camera with a narrow field of view. We first train a neuralnetwork with LSTM units in a 3D simulator of mobile robots to approximate theQ-value function in double DRQN. We also use a curriculum learning strategy toaccelerate and stabilize the training process. Then we deploy the trained modelto a real robot to perform 3D obstacle avoidance in its navigation. We evaluatethe proposed approach both in the simulated environment and on a robot in thereal world. The experimental results show that the approach is efficient andeasy to be deployed, and it performs well for 3D obstacle avoidance with anarrow observation angle, which outperforms other existing DRL-based models by15.5% on success rate.

DRQN-based 3D Obstacle Avoidance with a Limited Field of View