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arXiv · 2108.05962

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

Yu'an Chen·Guangda Chen·Lifan Pan·Jun Ma·Yu Zhang·Yanyong Zhang·Jianmin Ji
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Abstract

In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonomous navigation for an indoor mobile robot using a depth camera with a narrow field of view. We first train a neural network with LSTM units in a 3D simulator of mobile robots to approximate the Q-value function in double DRQN. We also use a curriculum learning strategy to accelerate and stabilize the training process. Then we deploy the trained model to a real robot to perform 3D obstacle avoidance in its navigation. We evaluate the proposed approach both in the simulated environment and on a robot in the real world. The experimental results show that the approach is efficient and easy to be deployed, and it performs well for 3D obstacle avoidance with a narrow observation angle, which outperforms other existing DRL-based models by 15.5% on success rate.

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Yu'an Chen, Guangda Chen, Lifan Pan, Jun Ma, Yu Zhang, Yanyong Zhang, Jianmin Ji. 2021-08-12. DRQN-based 3D Obstacle Avoidance with a Limited Field of View. https://arxiv.org/abs/2108.05962

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