Hierarchical path planner combining probabilistic roadmap and deep deterministic policy gradient for unmanned ground vehicles with non-holonomic constraints-Reference-Cited by-同舟云学术

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Hierarchical path planner combining probabilistic roadmap and deep deterministic policy gradient for unmanned ground vehicles with non-holonomic constraints

Published:2024-05 Issue:8 Volume:361 Page:106821
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Fan Jie,Zhang Xudong^ORCID,Zheng Kun,Zou Yuan,Zhou Nana

Publisher

Elsevier BV

Reference48 articles.

1. Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach;Zou;J. Supercomput.,2023

2. A machine learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments;Das;Comput. Electr. Eng.,2022

3. A high-precision LiDAR-inertial odometry via Kalman filter and factor graph optimization;Tang;IEEE Sens. J.,2023

4. Driving assistance system based on data fusion of multisource sensors for autonomous unmanned ground vehicles;Yang;Comput. Netw.,2021

5. Real‐time keypoints detection for autonomous recovery of the unmanned ground vehicle;Li;IET Image Process.,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning;Machines;2024-04-26

2. High-Speed Racing Reinforcement Learning Network: Learning the Environment Using Scene Graphs;IEEE Access;2024

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