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Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning.

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Název: Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning.
Autoři: Li, Dong, Yang, Panfei
Zdroj: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11282, 15p
Témata: DEEP reinforcement learning, REINFORCEMENT learning, DRONE aircraft, TELECOMMUNICATION systems, TEST methods
Abstrakt: In the process of the collaborative work of Unmanned Aerial Vehicle (UAV) clusters, the cluster communication node test is often carried out by a single-node test, which leads to poor topology and robustness of the overall network system, an imbalanced communication network load, and high complexity of the communication test, which seriously affects the diversified needs of current users and the efficiency of large-scale task processing. To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. The system management node is used to monitor the status of the UAV testing task execution node and bandwidth resources. By taking advantage of the method of continuous interaction between the agent and the environment, the future state of the node after processing the current task to be assigned is predicted and evaluated from the perspective of interpretability, so as to achieve the effectiveness and stability of the UAV cluster testing task collaborative execution. The experimental results show that our proposed method can ensure the stable operation of the UAV cluster, accurately predict the future state, and reduce the load degree and bandwidth resource consumption of the large-scale test task network system. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Dong%22">Li, Dong</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Panfei%22">Yang, Panfei</searchLink>
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  Data: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11282, 15p
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  Data: <searchLink fieldCode="DE" term="%22DEEP+reinforcement+learning%22">DEEP reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22REINFORCEMENT+learning%22">REINFORCEMENT learning</searchLink><br /><searchLink fieldCode="DE" term="%22DRONE+aircraft%22">DRONE aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22TELECOMMUNICATION+systems%22">TELECOMMUNICATION systems</searchLink><br /><searchLink fieldCode="DE" term="%22TEST+methods%22">TEST methods</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In the process of the collaborative work of Unmanned Aerial Vehicle (UAV) clusters, the cluster communication node test is often carried out by a single-node test, which leads to poor topology and robustness of the overall network system, an imbalanced communication network load, and high complexity of the communication test, which seriously affects the diversified needs of current users and the efficiency of large-scale task processing. To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. The system management node is used to monitor the status of the UAV testing task execution node and bandwidth resources. By taking advantage of the method of continuous interaction between the agent and the environment, the future state of the node after processing the current task to be assigned is predicted and evaluated from the perspective of interpretability, so as to achieve the effectiveness and stability of the UAV cluster testing task collaborative execution. The experimental results show that our proposed method can ensure the stable operation of the UAV cluster, accurately predict the future state, and reduce the load degree and bandwidth resource consumption of the large-scale test task network system. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: DEEP reinforcement learning
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      – SubjectFull: REINFORCEMENT learning
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      – SubjectFull: DRONE aircraft
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      – SubjectFull: TELECOMMUNICATION systems
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              Text: Dec2024
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