用深度学习让无人机在城市中自动避障
Learning Obstacle Avoidance using Double DQN for Quadcopter Navigation
- 用双DQN强化学习控制无人机
- 在模拟城市环境里实现零碰撞导航
- 适合做自主飞行系统研究的开发者
自主飞行器在城市环境中面临可靠导航的挑战。全球定位系统（GPS）精度下降、空间狭窄以及动态障碍物等因素使飞行机器人的路径规划变得复杂。飞行器有效导航所需的关键技能之一是利用机载深度传感器信息实现碰撞规避。本文提出一种基于强化学习的虚拟四旋翼无人机代理，配备深度摄像头，在模拟城市环境中进行导航。该方法通过双深度Q网络（Double DQN）训练无人机自主避障，提升在复杂环境中的适应能力。
原文摘要 · Abstract (English)
One of the challenges faced by Autonomous Aerial Vehicles is reliable navigation through urban environments. Factors like reduction in precision of Global Positioning System (GPS), narrow spaces and dynamically moving obstacles make the path planning of an aerial robot a complicated task. One of the skills required for the agent to effectively navigate through such an environment is to develop an ability to avoid collisions using information from onboard depth sensors. In this paper, we propose Reinforcement Learning of a virtual quadcopter robot agent equipped with a Depth Camera to navigate through a simulated urban environment.
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