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UAV path planning method for data collection of fixed-point equipment in complex forest environment

In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. Th...

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Autores principales: Cui, Xiaohui, Wang, Yu, Yang, Shijie, Liu, Hanzhang, Mou, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813396/
https://www.ncbi.nlm.nih.gov/pubmed/36620485
http://dx.doi.org/10.3389/fnbot.2022.1105177
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author Cui, Xiaohui
Wang, Yu
Yang, Shijie
Liu, Hanzhang
Mou, Chao
author_facet Cui, Xiaohui
Wang, Yu
Yang, Shijie
Liu, Hanzhang
Mou, Chao
author_sort Cui, Xiaohui
collection PubMed
description In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach.
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spelling pubmed-98133962023-01-06 UAV path planning method for data collection of fixed-point equipment in complex forest environment Cui, Xiaohui Wang, Yu Yang, Shijie Liu, Hanzhang Mou, Chao Front Neurorobot Neuroscience In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach. Frontiers Media S.A. 2022-12-22 /pmc/articles/PMC9813396/ /pubmed/36620485 http://dx.doi.org/10.3389/fnbot.2022.1105177 Text en Copyright © 2022 Cui, Wang, Yang, Liu and Mou. 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
Cui, Xiaohui
Wang, Yu
Yang, Shijie
Liu, Hanzhang
Mou, Chao
UAV path planning method for data collection of fixed-point equipment in complex forest environment
title UAV path planning method for data collection of fixed-point equipment in complex forest environment
title_full UAV path planning method for data collection of fixed-point equipment in complex forest environment
title_fullStr UAV path planning method for data collection of fixed-point equipment in complex forest environment
title_full_unstemmed UAV path planning method for data collection of fixed-point equipment in complex forest environment
title_short UAV path planning method for data collection of fixed-point equipment in complex forest environment
title_sort uav path planning method for data collection of fixed-point equipment in complex forest environment
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813396/
https://www.ncbi.nlm.nih.gov/pubmed/36620485
http://dx.doi.org/10.3389/fnbot.2022.1105177
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