Research on breakthrough and innovation of UAV mission planning method based on cloud computing-based reinforcement learning algorithm-Reference-Cited by-同舟云学术

Research on breakthrough and innovation of UAV mission planning method based on cloud computing-based reinforcement learning algorithm

Published:2019-10-09 Issue:3 Volume:37 Page:3285-3292
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Liu Rong¹,Liang Jin²,Alkhambashi Majid³

Affiliation:

1. UAV Research Institute of Nanjing University of Aeronautics and Astronautics, Middle and Small Size UAV Advanced Technique Key Laboratory of Ministry of Industry and Information Technology, Nanjing, Jiangsu, China

2. Science and Technology on Aircraft Control Laboratory, FACRI, Xi’an, Shanxi, China

3. Department of Information Technology, Al-Zahra College for Women, Muscat, Oman

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference29 articles.

1. Hovering control of 1-axial drone with reinforcement learning;Lee;Journal of Korea Multimedia Society,2018

2. Cooperative and distributed reinforcement learning of drones for field coverage;Pham;Finance Market,2018

3. Motor anomaly detection for unmanned aerial vehicles using reinforcement learning;Lu;IEEE Internet of Things Journal,2017

4. ADDSEN: Adaptive data processing and dissemination for drone swarms in urban sensing;Wu;IEEE Transactions on Computers,2017

5. ScapeGoat: Spotting abnormal resource usage in component-based reconfigurable software systems;Gonzalez-Herrera;Journal of Systems & Software,2016

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1. Deep Reinforcement Learning Based UAV Mission Planning with Charging Module;Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things;2023-05-26

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3. Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review;Artificial Intelligence Review;2022-09-26

4. Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q-Learning;Journal of Aerospace Information Systems;2021-04