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

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.

Automated summary

The study proposes a deep reinforcement learning-based framework for safe autonomous navigation of quadrotors in semi-known environments. The framework utilizes dueling double deep recurrent Q-learning for global path planning and contrastive learning-based feature extraction for real-time autonomous obstacle avoidance. Experimental results show remarkable performance in both global path planning and autonomous obstacle avoidance.