Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field

Reducing Oscillations for Obstacle Avoidance in a Dense Environment Using Deep Reinforcement Learning and Time-Derivative of an Artificial Potential Field

Obstacle avoidance plays a crucial role in ensuring the safe path planning of quadrotor unmanned aerial vehicles (QUAVs). In this study, we propose a hierarchical framework for obstacle avoidance, which combines the use of artificial potential field (APF) and deep reinforcement learning (DRL) for tr...

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Bibliographic Details
Main Authors: Zhilong Xi, Haoran Han, Jian Cheng, Maolong Lv
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Drones
Subjects:
quadrotor unmanned aerial vehicle (QUAV)
obstacle avoidance
artificial potential field (APF)
deep reinforcement learning (DRL)
oscillations
Online Access:https://www.mdpi.com/2504-446X/8/3/85

Internet

https://www.mdpi.com/2504-446X/8/3/85

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