论文标题
基于对决双眼Q学习的自主四项障碍避免避免
Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q-learning with monocular vision
论文作者
论文摘要
无人驾驶汽车（UAV）的快速发展提出了更高的自主障碍避免措施的要求。由于有效载荷和电源有限，小型无人机（例如四型）通常带有简单的传感器和计算单元，这使得传统方法更具挑战性。在本文中，证明了一个新颖的框架可以控制一个四型在拥挤的环境中以单眼视力自动飞行的框架。该框架采用了两个阶段的体系结构，该体系结构由传感模块和决策模块组成。传感模块基于一种无监督的深度学习方法。决策模块使用Dueling Double Deep Recurrent Q-Learning来消除板载单眼相机有限观察能力的不利影响。该框架使四极管无需任何以前的环境信息或标记的培训数据集实现自主障碍。训练有素的模型在模拟中显示出很高的成功率，并具有良好的转换场景概括能力。
The rapid development of unmanned aerial vehicles (UAV) puts forward a higher requirement for autonomous obstacle avoidance. Due to the limited payload and power supply, small UAVs such as quadrotors usually carry simple sensors and computation units, which makes traditional methods more challenging to implement. In this paper, a novel framework is demonstrated to control a quadrotor flying through crowded environments autonomously with monocular vision. The framework adopts a two-stage architecture, consisting of a sensing module and a decision module. The sensing module is based on an unsupervised deep learning method. And the decision module uses dueling double deep recurrent Q-learning to eliminate the adverse effects of limited observation capacity of an on-board monocular camera. The framework enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets for training. The trained model shows a high success rate in the simulation and a good generalization ability for transformed scenarios.
