Evidence map›Paper›PMID 37228696›Full record

ArticleJournal of ambient intelligence and humanized computing2023

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment.

Yuhang Han, Hongyu Xiang, Jianing Cao, Xiaohua Yang, Nan Pan, Linhai Huang

Abstract read
In one paragraph

Article in Journal of ambient intelligence and humanized computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuhang HanMechanical Engineering, Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Yunnan, China.
Hongyu XiangMechanical Engineering, Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Yunnan, China.
Jianing CaoMechanical Engineering, Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Yunnan, China.
Xiaohua YangComputer Science and Technology, Metrology Center, Yunnan Power Grid Co., Ltd., Yunnan, China.
Nan PanMechanical Engineering, Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Yunnan, China.ORCID 0000-0002-8155-2282
Linhai HuangComputer Science and Technology, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs' impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.

Indexed as

COVID-19Golden eagle optimization algorithmTrajectory optimizationUAV delivery

Identifiers

PMID37228696
PMCPMC10027271

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.