ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Computational Intelligence in Robotics
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1645927
This article is part of the Research TopicGenerative AI and Intelligent Control in Robotics for Deployment in Challenging EnvironmentsView all articles
Intelligent Maneuver Decision-Making of UAV Based on TD3-LSTM Reinforcement Learning Algorithm under Uncertain Information
Provisionally accepted
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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Aiming to address the complexity and uncertainty of UAV aerial confrontation, a Twin Delayed Deep Deterministic Policy Gradient (TD3)-Long Short Term Memory (LSTM) reinforcement learning-based intelligent maneuver decision-making method is developed in this paper. A victory/defeat adjudication model is established considering the operational capability of UAVs based on an aerial confrontation scenario and the 3-DOF UAV model. For the purpose of assisting UAV in making maneuvering decisions in continuous action space, a model-driven state transition update mechanism is designed. The uncertainty is represented using the Wasserstein distance and memory nominal distribution methods to estimate the detection noise of the target. On the basis of TD3, an LSTM network is utilized to extract features from high-dimensional aerial confrontation situations with uncertainty. The effectiveness of the proposed method is verified by conducting four different aerial confrontation simulation experiments.
Keywords: UAV, Maneuver decision-making, reinforcement learning, TD3, LSTM
Received: 12 Jun 2025; Accepted: 02 Jul 2025.
Copyright: © 2025 Zhou, Liu, Jin and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tongle Zhou, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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