Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784883275463917568 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9902498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lijie transutpapsoandmaddpgbasedmultiuavstrajectoryplanningalgorithmforemergencycommunication AT caoshuang transutpapsoandmaddpgbasedmultiuavstrajectoryplanningalgorithmforemergencycommunication AT liuxianjie transutpapsoandmaddpgbasedmultiuavstrajectoryplanningalgorithmforemergencycommunication AT yuruiyun transutpapsoandmaddpgbasedmultiuavstrajectoryplanningalgorithmforemergencycommunication AT wangxingwei transutpapsoandmaddpgbasedmultiuavstrajectoryplanningalgorithmforemergencycommunication |