Path Planning for Unmanned Aerial Vehicle via Off-Policy Reinforcement Learning With Enhanced Exploration-Reference-Cited by-同舟云学术

Path Planning for Unmanned Aerial Vehicle via Off-Policy Reinforcement Learning With Enhanced Exploration

Published:2024-06 Issue:3 Volume:8 Page:2625-2639
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Wang Zhengjun¹^ORCID,Gao Weifeng¹^ORCID,Li Genghui²^ORCID,Wang Zhenkun³^ORCID,Gong Maoguo⁴^ORCID

Affiliation:

1. School of Mathematics and Statistics, Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi'an, China

2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

3. School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China

4. Key Laboratory of Intelligent Perception and Image Understanding, International Research Center for Intelligent Perception and Computation, Ministry of Education, Xidian University, Xi'an, China

Funder

National Natural Science Foundation of China

Natural Science Basic Research Plan in Shaanxi Province of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10538459/10478216.pdf?arnumber=10478216

Reference59 articles.

1. Task Offloading for Post-Disaster Rescue in Unmanned Aerial Vehicles Networks

2. Moving peak drone search problem: An online multi-swarm intelligence approach for UAV search operations

3. Asynchronous Multithreading Reinforcement-Learning-Based Path Planning and Tracking for Unmanned Underwater Vehicle

4. The Right Path: Comprehensive Path Planning for Lunar Exploration Rovers

5. An algorithm for planning collision-free paths among polyhedral obstacles

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