狭視野深度カメラを用いたDRQNによる3次元障害物回避
DRQN-based 3D Obstacle Avoidance with a Limited Field of View
狭い視野の深度カメラを持つ屋内移動ロボットのため、LSTM付き二重DRQNとカリキュラム学習で3次元障害物回避を学習し、実機でも検証した研究。
著者: Yu'an Chen, Guangda Chen, Lifan Pan, Jun Ma, Yu Zhang, Yanyong Zhang, Jianmin Ji
分類: cs.RO
原文アブストラクト
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.
