Monocular Vision based Autonomous Landing of Quadrotor through Deep Reinforcement Learning
作者单位：College of Mechatronic Engineering and AutomationNational University of Defense Technology
会议名称：《第37届中国控制会议》
会议日期：2018年
学科分类：08[工学] 081105[工学-导航、制导与控制] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程]
基 金：supported by National Natural Science Foundation(NNSF)of China under Grant 61403406
关 键 词：Unmanned Aerial Vehicle(UAV) Autonomous Landing Deep Reinforcement Learning(DRL) DQN
摘 要：An improved deep reinforcement learning（DRL） method is proposed to solve autonomous landing problem of quadrotor. Autonomous landing is a significant function for unmanned aerial vehicle（UAV） such as quadrotor. Previous solutions are mainly based on relative position calculation or the landmark detection, which either needs massive additional sensors or lacks intelligence. In this paper, we focus on realizing autonomous landing through DRL method. Whole landing process is implemented by an improved deep Q-learning network（DQN） based end-to-end control scheme. Only one down-looking camera is used to capture raw images directly as input states. An Aruco tag is placed at the landing region for feature extraction. Double network and the dueling architecture are applied to improve DQN algorithm. Besides, the reward function is well designed to fit the auto-landing scenario. The experiments show that the improved DQN can make the quadrotor land on the landmark successfully and achieve better performance while comparing to the original deep Q-learning solution.