{"title":"动态环境下分布式多无人机兴趣点覆盖","authors":"Fatih Aydemir, Aydın Çetin","doi":"10.2298/csis221222037a","DOIUrl":null,"url":null,"abstract":"Mobile agents, which learn to optimize a task in real time, can adapt to dynamic environments and find the optimum locations with the navigation mechanism that includes a motion model. In this study, it is aimed to effectively cover points of interest (PoI) in a dynamic environment by modeling a group of unmanned aerial vehicles (UAVs) on the basis of a learning multi-agent system. Agents create an abstract rectangular plane containing the area to be covered, and then decompose the area into grids. An agent learns to locate on a center of grid that are closest to it, which has the largest number of PoIs to plan its path. This planning helps to achieve a high fairness index by reducing the number of common PoIs covered. The proposed method has been tested in a simulation environment and the results are presented by comparing with similar studies. The results show that the proposed method outperforms existing similar studies and is suitable for area coverage applications.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"16 1","pages":"1061-1084"},"PeriodicalIF":1.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Point of interest coverage with distributed multi-unmanned aerial vehicles on dynamic environment\",\"authors\":\"Fatih Aydemir, Aydın Çetin\",\"doi\":\"10.2298/csis221222037a\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobile agents, which learn to optimize a task in real time, can adapt to dynamic environments and find the optimum locations with the navigation mechanism that includes a motion model. In this study, it is aimed to effectively cover points of interest (PoI) in a dynamic environment by modeling a group of unmanned aerial vehicles (UAVs) on the basis of a learning multi-agent system. Agents create an abstract rectangular plane containing the area to be covered, and then decompose the area into grids. An agent learns to locate on a center of grid that are closest to it, which has the largest number of PoIs to plan its path. This planning helps to achieve a high fairness index by reducing the number of common PoIs covered. The proposed method has been tested in a simulation environment and the results are presented by comparing with similar studies. The results show that the proposed method outperforms existing similar studies and is suitable for area coverage applications.\",\"PeriodicalId\":50636,\"journal\":{\"name\":\"Computer Science and Information Systems\",\"volume\":\"16 1\",\"pages\":\"1061-1084\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science and Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.2298/csis221222037a\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science and Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.2298/csis221222037a","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Point of interest coverage with distributed multi-unmanned aerial vehicles on dynamic environment
Mobile agents, which learn to optimize a task in real time, can adapt to dynamic environments and find the optimum locations with the navigation mechanism that includes a motion model. In this study, it is aimed to effectively cover points of interest (PoI) in a dynamic environment by modeling a group of unmanned aerial vehicles (UAVs) on the basis of a learning multi-agent system. Agents create an abstract rectangular plane containing the area to be covered, and then decompose the area into grids. An agent learns to locate on a center of grid that are closest to it, which has the largest number of PoIs to plan its path. This planning helps to achieve a high fairness index by reducing the number of common PoIs covered. The proposed method has been tested in a simulation environment and the results are presented by comparing with similar studies. The results show that the proposed method outperforms existing similar studies and is suitable for area coverage applications.
期刊介绍:
About the journal
Home page
Contact information
Aims and scope
Indexing information
Editorial policies
ComSIS consortium
Journal boards
Managing board
For authors
Information for contributors
Paper submission
Article submission through OJS
Copyright transfer form
Download section
For readers
Forthcoming articles
Current issue
Archive
Subscription
For reviewers
View and review submissions
News
Journal''s Facebook page
Call for special issue
New issue notification
Aims and scope
Computer Science and Information Systems (ComSIS) is an international refereed journal, published in Serbia. The objective of ComSIS is to communicate important research and development results in the areas of computer science, software engineering, and information systems.