使用双DQN学习避障以实现四旋翼导航
Learning Obstacle Avoidance using Double DQN for Quadcopter Navigation
AI总结：
本文提出使用双DQN算法，通过深度相机实现四旋翼机器人在模拟城市环境中的避障导航学习。
摘要：
自主空中车辆在城市环境中可靠导航面临挑战。全球定位系统（GPS）精度下降、狭窄空间和动态障碍物等因素使空中机器人的路径规划变得复杂。为了使智能体有效导航，其需要具备利用机载深度传感器信息避障的能力。本文提出了一种配备深度相机的虚拟四旋翼机器人智能体的强化学习方法，用于在模拟城市环境中导航。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
