Multi-drone rescue search in a large network

IF 6 2区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE European Journal of Operational Research Pub Date : 2025-08-01 Epub Date: 2025-02-11 DOI:10.1016/j.ejor.2025.02.003
Victor Gonzalez , Patrick Jaillet
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引用次数: 0

Abstract

Natural disasters are recurring emergencies that can result in numerous deaths and injuries. When a natural disaster occurs, rescue teams can be sent to help affected survivors, but deploying them efficiently is a challenge. Rescuers not knowing where affected survivors are located poses a significant challenge in delivering aid. With the development of new technologies, there are new possibilities to reduce this uncertainty, alleviating this challenge. One can first send out automated drones to locate affected survivors and then send rescue teams to their locations. We develop a model for the search process and construct mathematical methods to construct efficient search routes. We utilize a divide and conquer technique to determine the routes that are most likely to yield an efficient search. We combine this with our mathematical methods to construct efficient search routes in real-time and a method to update these routes in real-time as drones gather information.
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多无人机大网络救援搜索
自然灾害是经常发生的紧急情况,可造成大量伤亡。当自然灾害发生时,救援队可以被派去帮助受影响的幸存者,但有效地部署他们是一个挑战。救援人员不知道受灾幸存者的位置,这对提供援助构成了重大挑战。随着新技术的发展,有新的可能性来减少这种不确定性,减轻这一挑战。首先可以派出自动无人机定位受影响的幸存者,然后派遣救援队前往他们的地点。我们建立了一个搜索过程模型,并构建了数学方法来构建有效的搜索路径。我们使用分而治之的技术来确定最有可能产生有效搜索的路线。我们将其与数学方法相结合,构建了有效的实时搜索路线,并在无人机收集信息时实时更新这些路线。
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来源期刊
European Journal of Operational Research
European Journal of Operational Research 管理科学-运筹学与管理科学
CiteScore
11.90
自引率
9.40%
发文量
786
审稿时长
8.2 months
期刊介绍: The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making.
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