Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning-Reference-Cited by-同舟云学术

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Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning

Published:2021-10 Issue: Volume: Page:
ISSN:1000-9361
Container-title:Chinese Journal of Aeronautics
language:en
Short-container-title:Chinese Journal of Aeronautics

Author:

ZHOU Wenhong,LI Jie,LIU Zhihong,SHEN Lincheng

Funder

National Natural Science Foundation of China

National University of Defense Technology

Publisher

Elsevier BV

Subject

Mechanical Engineering,Aerospace Engineering

Reference52 articles.

1. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning;Roberge;IEEE Trans Ind Inform,2013

2. Bio-inspired algorithms for autonomous deployment and localization of sensor nodes;Kulkarni;IEEE Trans Syst Man Cybern C Appl Rev,2010

3. Kuriki Y, Namerikawa T. Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control. 2015 European control conference (ECC); 2015 July 15-17; Linz, Austria. Piscataway: IEEE Press; 2015.p.3079–84.

4. Wu XL, Yang ZC, Huo JN, et al. UAV formation control based on consistency. 2015 7th international conference on modelling, identification and control (ICMIC); 2015 December 18-20; Sousse, Tunisia. Piscataway: IEEE Press; 2015.p.1–5.

5. Cooperative path planning with applications to target tracking and obstacle avoidance for multi-UAVs;Yao;Aerosp Sci Technol,2016

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