Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning

Unmanned Aerial Vehicle Path Planning in Complex Dynamic Environments Based on Deep Reinforcement Learning

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort t...

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Bibliographic Details
Main Authors: Jiandong Liu, Wei Luo, Guoqing Zhang, Ruihao Li
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Machines
Subjects:
UAV obstacle avoidance
artificial potential field
dynamic environment
DQN algorithm
Yolov8
Online Access:https://www.mdpi.com/2075-1702/13/2/162
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