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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Bibliographic Details
Main Authors: Da Jiang, Ling Du, Shuhui Li, Meijing Wang, Hongchao Zhang, Xiaole Chen, Yunlong Sun
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Adaptive motion planning
dynamic window approach
deep reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/10459192/

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