عنوان انگلیسی مقاله:
UAV navigation in high dynamic environments: A deep reinforcement learning approach
ترجمه فارسی عنوان مقاله:
هدایت پهپاد در محیط های پویا بالا: یک رویکرد یادگیری تقویتی عمیق
Sciencedirect - Elsevier - Chinese Journal of Aeronautics, Uncorrected proof. doi:10.1016/j.cja.2020.05.011
Tong GUOa,b, Nan JIANG a, Biyue LI a,b, Xi ZHUc, Ya WANGd,e,*, 6 Wenbo DUa,
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.
KEYWORDS : Autonomous vehicles | Deep learning | Motion planning | Navigation | Reinforcement learning | Unmanned Aerial Vehicle (UAV)