Cognitive UAV tracking: Leveraging DRL and hybrid curriculum learning for target reacquisition

Wang, Jiaqing, Zeng, Baichuan, Deng, Lan, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902, Wei, Changyun and Zeng, Zheng 2025. Cognitive UAV tracking: Leveraging DRL and hybrid curriculum learning for target reacquisition. IEEE Transactions on Automation Science and Engineering 10.1109/TASE.2025.3577984

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Abstract

Tracking a moving unmanned ground vehicle (UGV) with an autonomous Unmanned Aerial Vehicle (UAV) is challenging, particularly in GNSS-denied indoor environments where reacquiring the UGV after losing track poses a significant obstacle. This paper presents a novel learning framework designed to address these challenges, enabling a quadrotor UAV to effectively chase a moving UGV and regain tracking in an indoor environment. The proposed framework encompasses two primary components: the Track-HCL and the Tracking Vision System (TVS). The TVS leverages a lightweight tracker to offer real-time recognition and localization of the UGV. Additionally, the Chronological Ghosting (CG) method is employed to describe the UGV’s motion trend within a single frame. The Track-HCL component introduces a hybrid curriculum strategy to guide policy learning for the Deep Reinforcement Learning (DRL) agent. The Track-HCL enables the agent to learn the tracking policy conducive to target chasing and proficient reacquisition. We demonstrate the effectiveness of the proposed method in both simulation and field experiments.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1545-5955
Date of First Compliant Deposit:	12 June 2025
Date of Acceptance:	4 June 2025
Last Modified:	18 Jun 2025 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/179044

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