Persistent target tracking in urban environments using UAV is a difficult task due to the limited field of view, visibility obstruction from obstacles and uncertain target motion. The vehicle needs to plan intelligently in 3D such that the target visibility is maximized. In this paper, we introduce Target Following DQN (TF-DQN), a deep reinforcement learning technique based on Deep Q-Networks with a curriculum training framework for the UAV to persistently track the target in the presence of obstacles and target motion uncertainty. The algorithm is evaluated through simulations. The results show that the UAV tracks the target persistently in diverse environments while avoiding obstacles on the trained environments as well as on unseen environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning


    Contributors:


    Publication date :

    2020-09-01


    Size :

    899498 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Target Tracking Control of UAV Through Deep Reinforcement Learning

    Ma, Bodi / Liu, Zhenbao / Zhao, Wen et al. | IEEE | 2023


    Multi-UAV Target-Finding in Simulated Indoor Environments using Deep Reinforcement Learning

    Walker, Ory / Vanegas, Fernando / Gonzalez, Felipe et al. | IEEE | 2020


    Navigation in Urban Environments amongst pedestrians using Multi-Objective Deep Reinforcement Learning

    Deshpande, Niranjan / Vaufreydaz, Dominique / Spalanzani, Anne | IEEE | 2021


    Deep-Reinforcement-Learning-Based Radar Parameter Adaptation for Multiple-Target Tracking

    Huang, Yongbing / Guo, Rui / Zhang, Yue et al. | IEEE | 2024


    Deep Reinforcement Learning Controller for Autonomous Tracking of Evasive Ground Target

    Wijk, David Van / Eves, Kameron J. / Valasek, John | TIBKAT | 2023