Autonomous Navigation for Cellular-Connected UAV in Highly Dynamic Environments: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Autonomous Navigation for Cellular-Connected UAV in Highly Dynamic Environments: A Deep Reinforcement Learning Approach

Published:2024-09 Issue:5 Volume:37 Page:
ISSN:0893-1321
Container-title:Journal of Aerospace Engineering
language:en
Short-container-title:J. Aerosp. Eng.

Author:

Wu Di¹^ORCID,Shi Zhiyi¹^ORCID,Zhang Yibo²,Huang Mengxing³

Affiliation:

1. School of Information and Communication Engineering, Hainan Univ., Haikou 570228, China. ORCID: .

2. Dept. of Automation, Shanghai Jiao Tong Univ., Shanghai 200240, China (corresponding author).

3. Professor, School of Information and Communication Engineering, Hainan Univ., Haikou 570228, China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JAEEEZ.ASENG-5265

Reference38 articles.

1. Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach

2. Joint Power Allocation and Placement Scheme for UAV-Assisted IoT With QoS Guarantee

3. Multi-Agent Reinforcement Learning With Policy Clipping and Average Evaluation for UAV-Assisted Communication Markov Game

4. Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

5. UAV navigation in high dynamic environments: A deep reinforcement learning approach

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