UAV navigation in high dynamic environments:A deep reinforcement learning approach  

作　　者：Tong GUO Nan JIANG Biyue LI Xi ZHU Ya WANG Wenbo DU 

机构地区：[1]School of Electronic and Information Engineering,Beihang University,Bejing 100083,China [2]Key Laboratory of Advanced Technology of Near Space Information System(Beihang University),Ministry of Industry and Information Technology of China,Beijing 100083,China [3]Research Institute of Frontier Science,Beihang University,Beijing 100083,China [4]College of Software,Beihang University,Beijing 100083,China [5]State Key Laboratory of Software Development Environment,Beihang University,Beijing 100083,China

出　　处：《Chinese Journal of Aeronautics》2021年第2期479-489,共11页中国航空学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China (Nos. 61671031, 61722102, and91738301)。

摘　　要：Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning(DRL) methods have shown promise for addressing the UAV navigation problem,but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast.In this work, we propose an improved DRL-based method to tackle these fundamental limitations.To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory(LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.

关 键 词：Autonomous vehicles Deep learning Motion planning NAVIGATION Reinforcement learning Unmanned Aerial Vehicle(UAV) 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249.3