Riheng Jia , Hengchao Li , Peifa Sun , Zhonglong Zheng , Minglu Li
{"title":"基于矩阵差分进化的移动网络视觉覆盖无人机轨迹优化","authors":"Riheng Jia , Hengchao Li , Peifa Sun , Zhonglong Zheng , Minglu Li","doi":"10.1016/j.knosys.2025.113797","DOIUrl":null,"url":null,"abstract":"<div><div>In this work, we develop a novel trajectory optimization approach for an unmanned aerial vehicle (UAV) to achieve efficient visual coverage of terrestrial mobile nodes. Unlike most existing studies, we consider the node mobility and generate a continuous and smooth UAV trajectory, which more suits the practical scenario. We aim to maximize the total number of visually covered nodes during the UAV’s mission, by appropriately designing the UAV’s three-dimensional (3D) flight trajectory. To handle the infinite solution space, we first leverage the Bézier curve method to transform the continuous trajectory optimization problem into a discrete control point selection problem, reducing the computational complexity while preserving the trajectory smoothness. Then, we develop a novel UAV trajectory optimization algorithm by employing the matrix-based differential evolution (MDE) framework, which can maximize the number of visually covered nodes with the reduced computational complexity. Extensive simulation results validate the effectiveness and superiority of our approach, compared with existing arts.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"324 ","pages":"Article 113797"},"PeriodicalIF":7.6000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UAV trajectory optimization for visual coverage in mobile networks using matrix-based differential evolution\",\"authors\":\"Riheng Jia , Hengchao Li , Peifa Sun , Zhonglong Zheng , Minglu Li\",\"doi\":\"10.1016/j.knosys.2025.113797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this work, we develop a novel trajectory optimization approach for an unmanned aerial vehicle (UAV) to achieve efficient visual coverage of terrestrial mobile nodes. Unlike most existing studies, we consider the node mobility and generate a continuous and smooth UAV trajectory, which more suits the practical scenario. We aim to maximize the total number of visually covered nodes during the UAV’s mission, by appropriately designing the UAV’s three-dimensional (3D) flight trajectory. To handle the infinite solution space, we first leverage the Bézier curve method to transform the continuous trajectory optimization problem into a discrete control point selection problem, reducing the computational complexity while preserving the trajectory smoothness. Then, we develop a novel UAV trajectory optimization algorithm by employing the matrix-based differential evolution (MDE) framework, which can maximize the number of visually covered nodes with the reduced computational complexity. Extensive simulation results validate the effectiveness and superiority of our approach, compared with existing arts.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":\"324 \",\"pages\":\"Article 113797\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705125008433\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705125008433","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
UAV trajectory optimization for visual coverage in mobile networks using matrix-based differential evolution
In this work, we develop a novel trajectory optimization approach for an unmanned aerial vehicle (UAV) to achieve efficient visual coverage of terrestrial mobile nodes. Unlike most existing studies, we consider the node mobility and generate a continuous and smooth UAV trajectory, which more suits the practical scenario. We aim to maximize the total number of visually covered nodes during the UAV’s mission, by appropriately designing the UAV’s three-dimensional (3D) flight trajectory. To handle the infinite solution space, we first leverage the Bézier curve method to transform the continuous trajectory optimization problem into a discrete control point selection problem, reducing the computational complexity while preserving the trajectory smoothness. Then, we develop a novel UAV trajectory optimization algorithm by employing the matrix-based differential evolution (MDE) framework, which can maximize the number of visually covered nodes with the reduced computational complexity. Extensive simulation results validate the effectiveness and superiority of our approach, compared with existing arts.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.