Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments  

作　　者：Fei WANG Xiaoping ZHU Zhou ZHOU Yang TANG 

机构地区：[1]School of Astronautics,Northwestern Polytechnical University,Xi’an 710072,China [2]School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China

出　　处：《Chinese Journal of Aeronautics》2024年第3期237-257,共21页中国航空学报（英文版）

摘　　要：In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

关 键 词：Faster R-CNN model Replay memory Data Deposit Mechanism(DDM) Two-part training approach Image-based Autonomous Navigation and Collision Avoidance(ANCA) Unmanned Aerial Vehicle(UAV) 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249.3