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Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning
Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767661/ https://www.ncbi.nlm.nih.gov/pubmed/31540537 http://dx.doi.org/10.3390/s19184032 |
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author | Qin, Zhen Li, Aijing Dong, Chao Dai, Haipeng Xu, Zhengqin |
author_facet | Qin, Zhen Li, Aijing Dong, Chao Dai, Haipeng Xu, Zhengqin |
author_sort | Qin, Zhen |
collection | PubMed |
description | Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms. |
format | Online Article Text |
id | pubmed-6767661 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67676612019-10-02 Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning Qin, Zhen Li, Aijing Dong, Chao Dai, Haipeng Xu, Zhengqin Sensors (Basel) Article Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms. MDPI 2019-09-18 /pmc/articles/PMC6767661/ /pubmed/31540537 http://dx.doi.org/10.3390/s19184032 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qin, Zhen Li, Aijing Dong, Chao Dai, Haipeng Xu, Zhengqin Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning |
title | Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning |
title_full | Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning |
title_fullStr | Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning |
title_full_unstemmed | Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning |
title_short | Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning |
title_sort | completion time minimization for multi-uav information collection via trajectory planning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6767661/ https://www.ncbi.nlm.nih.gov/pubmed/31540537 http://dx.doi.org/10.3390/s19184032 |
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