TY - GEN
T1 - A Decentralized Cooperative Coverage Control for Networked Multiple UAVs Based on Deep Reinforcement Learning
AU - Cheng, Longbo
AU - Qu, Guixian
AU - Zhou, Jianshan
AU - Zhao, Dezong
AU - Qu, Kaige
AU - Sheng, Zhengguo
AU - Zhai, Junda
AU - Ren, Chenghao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Deployment of the unarmed aerial vehicle (UAV) swarm promises increased efficiency and safety of area search coverage. Multiple UAVs must be capable of autonomous collaboration and area search coverage for this. Therefore, we integrate multi-agent reinforcement learning into the cooperative control method of UAV swarm and propose a decentralized cooperative control for networked multiple UAVs technique based on the extended-proximal policy optimization algorithm (EPPO). The proposed approach not only adopts distributed training for multiple agents, but also allows them to obtain some mutual state information, such as position information and searched sub-areas. So, it can significantly speed up training and increase the effectiveness and safety of task completion in real-world applications. After a simulation, the multi-intelligent UAV can rapidly cover 100% of the mission area and ensure more excellent safety.
AB - Deployment of the unarmed aerial vehicle (UAV) swarm promises increased efficiency and safety of area search coverage. Multiple UAVs must be capable of autonomous collaboration and area search coverage for this. Therefore, we integrate multi-agent reinforcement learning into the cooperative control method of UAV swarm and propose a decentralized cooperative control for networked multiple UAVs technique based on the extended-proximal policy optimization algorithm (EPPO). The proposed approach not only adopts distributed training for multiple agents, but also allows them to obtain some mutual state information, such as position information and searched sub-areas. So, it can significantly speed up training and increase the effectiveness and safety of task completion in real-world applications. After a simulation, the multi-intelligent UAV can rapidly cover 100% of the mission area and ensure more excellent safety.
KW - Unmanned aerial vehicles
KW - cooperative coverage control
KW - decentralized algorithm
KW - multi-agent reinforcement learning
KW - proximal policy optimization
UR - https://www.scopus.com/pages/publications/85180129054
U2 - 10.1109/ICUS58632.2023.10318317
DO - 10.1109/ICUS58632.2023.10318317
M3 - 会议稿件
AN - SCOPUS:85180129054
T3 - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
SP - 205
EP - 210
BT - Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Unmanned Systems, ICUS 2023
Y2 - 13 October 2023 through 15 October 2023
ER -