Design and Application of an Intelligent Decision-Making System for Unmanned Aerial Vehicles Based on Deep Reinforcement Learning | Zendy

Xiaobo Song | Zendy

Open Access

Design and Application of an Intelligent Decision-Making System for Unmanned Aerial Vehicles Based on Deep Reinforcement Learning

Author(s) -

Xiaobo Song

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3615941

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper uses deep reinforcement learning algorithms to achieve intelligent decision-making for unmanned aerial vehicles (UAVs) to realize autonomous obstacle avoidance and target tracking of UAVs. This research proposes an improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) algorithm combined with a multi-experience pool mechanism. The UAV obstacle avoidance task is modeled as a Partially Observable Markov Decision Process (POMDP) model. The experimental results show that the ITD3 algorithm demonstrates significant advantages in the simulated flight environment, with the task success rate increasing to 93.7%, and it also has excellent anti-noise interference ability and generalization.

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