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Real-time autonomous obstacle avoidance method for UAV in a three-dimensional dynamic environment

Published online by Cambridge University Press:  17 November 2025

G. Li Open the ORCID record for G. Li [Opens in a new window] ,
L. Ling Open the ORCID record for L. Ling [Opens in a new window]  and
F. Xu
G. Li*
Affiliation:
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics , Nanjing, China Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Shenzhen, China
L. Ling
Affiliation:
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics , Nanjing, China
F. Xu
Affiliation:
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics , Nanjing, China
*
Corresponding author: G. Li; Email: liguiyi@nuaa.edu.cn
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Abstract

Reinforcement learning (RL) has demonstrated computational efficiency and autonomy in solving unmanned aerial vehicle (UAV) obstacle avoidance problems. However, practical applications still remain challenges, such as three-dimensional manoeuvres, dynamic obstacles and kinematic constraints. This paper proposes a real-time obstacle avoidance method based on RL and a kinematic model, where the RL framework outputs 3D-axis velocity to enable continuous UAV manoeuver control. To perceive large-scale, highly dynamic obstacles, we establish a 3D safety separation model and construct a modular observation matrix to enhance perception capability. The Soft Actor-Critic (SAC) algorithm is adopted to enhance stochastic exploration in high-dimensional state space while incorporating flight uncertainty. Under simulation, the proposed method effectively avoids dynamic obstacles. The optimised state space boosts learning speed and performance. This provides an effective solution for UAV autonomous obstacle avoidance in complex environments.

Keywords

air traffic managementreinforcement learningSoft-Actor-Critic algorithmthree-dimensional dynamic environment avoidanceUAV obstacle avoidance method

Information

Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 18
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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