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Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight...

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Autores principales: Li, Jie, Cao, Shuang, Liu, Xianjie, Yu, Ruiyun, Wang, Xingwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902498/
https://www.ncbi.nlm.nih.gov/pubmed/36760806
http://dx.doi.org/10.3389/fnbot.2022.1076338
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author Li, Jie
Cao, Shuang
Liu, Xianjie
Yu, Ruiyun
Wang, Xingwei
author_facet Li, Jie
Cao, Shuang
Liu, Xianjie
Yu, Ruiyun
Wang, Xingwei
author_sort Li, Jie
collection PubMed
description Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.
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spelling pubmed-99024982023-02-08 Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication Li, Jie Cao, Shuang Liu, Xianjie Yu, Ruiyun Wang, Xingwei Front Neurorobot Neuroscience Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm. Frontiers Media S.A. 2023-01-24 /pmc/articles/PMC9902498/ /pubmed/36760806 http://dx.doi.org/10.3389/fnbot.2022.1076338 Text en Copyright © 2023 Li, Cao, Liu, Yu and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Jie
Cao, Shuang
Liu, Xianjie
Yu, Ruiyun
Wang, Xingwei
Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication
title Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication
title_full Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication
title_fullStr Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication
title_full_unstemmed Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication
title_short Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication
title_sort trans-utpa: pso and maddpg based multi-uavs trajectory planning algorithm for emergency communication
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902498/
https://www.ncbi.nlm.nih.gov/pubmed/36760806
http://dx.doi.org/10.3389/fnbot.2022.1076338
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