{"title":"目标覆盖问题中自主多无人机系统的高效路径规划方法","authors":"V. Pehlivanoglu, Perihan Pehlivanoğlu","doi":"10.1108/aeat-10-2023-0258","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems.\n\n\nDesign/methodology/approach\nAn enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy.\n\n\nFindings\nNumerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively.\n\n\nPractical implications\nSimulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems.\n\n\nOriginality/value\nThe proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.\n","PeriodicalId":55540,"journal":{"name":"Aircraft Engineering and Aerospace Technology","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An efficient path planning approach for autonomous multi-UAV system in target coverage problems\",\"authors\":\"V. Pehlivanoglu, Perihan Pehlivanoğlu\",\"doi\":\"10.1108/aeat-10-2023-0258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThe purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems.\\n\\n\\nDesign/methodology/approach\\nAn enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy.\\n\\n\\nFindings\\nNumerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively.\\n\\n\\nPractical implications\\nSimulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems.\\n\\n\\nOriginality/value\\nThe proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.\\n\",\"PeriodicalId\":55540,\"journal\":{\"name\":\"Aircraft Engineering and Aerospace Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aircraft Engineering and Aerospace Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1108/aeat-10-2023-0258\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, AEROSPACE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aircraft Engineering and Aerospace Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/aeat-10-2023-0258","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
An efficient path planning approach for autonomous multi-UAV system in target coverage problems
Purpose
The purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems.
Design/methodology/approach
An enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy.
Findings
Numerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively.
Practical implications
Simulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems.
Originality/value
The proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.
期刊介绍:
Aircraft Engineering and Aerospace Technology provides a broad coverage of the materials and techniques employed in the aircraft and aerospace industry. Its international perspectives allow readers to keep up to date with current thinking and developments in critical areas such as coping with increasingly overcrowded airways, the development of new materials, recent breakthroughs in navigation technology - and more.