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,Yu’an Chen,Guangda Chen,Lifan Pan,Jun Ma,Yu Zhang,Yanyong Zhang,Jianmin Ji
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...
