Quadrotor Autonomous Navigation in Semi-Known Environments Based on Deep Reinforcement Learning

Remote Sensing

Wenjie Lou¹  Ming Zhu¹  Xiao Guo²  Jiajun Ou³ 

[1] Institute of Unmanned System, Beihang University, Beijing 100191, China;Research Institute for Frontier Science, Beihang University, Beijing 100191, China;School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China;

关键词: unmanned aerial vehicle;    path planning;    obstacle avoidance;    deep reinforcement learning;   

DOI  :  10.3390/rs13214330

来源: DOAJ

【 摘 要 】

In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.

【 授权许可】

Unknown