An Improved Dynamic Window Approach Based on Reinforcement Learning for the Trajectory Planning of Automated Guided Vehicles

An Improved Dynamic Window Approach Based on Reinforcement Learning for the Trajectory Planning of Automated Guided Vehicles

The traditional dynamic window approach (DWA) adopts the constant intervals for the sampling window, which limits the trajectory exploration possibility. This paper employs the twin delayed deep deterministic policy gradient (TD3) approach to generate a reinforcement-learning-based auxiliary candida...

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Detalhes bibliográficos
Principais autores: Da Jiang, Ling Du, Shuhui Li, Meijing Wang, Hongchao Zhang, Xiaole Chen, Yunlong Sun
Formato: Artigo
Idioma:English
Publicado em: IEEE 2024-01-01
coleção:IEEE Access
Assuntos:
Adaptive motion planning
dynamic window approach
deep reinforcement learning
Acesso em linha:https://ieeexplore.ieee.org/document/10459192/

Internet

https://ieeexplore.ieee.org/document/10459192/

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