Hanbin Zhang , Dalei Song , Jiangli Cao , Wenshan Yu , Wenchuan Zang
{"title":"使用无人水下航行器有效跟踪海洋涡流","authors":"Hanbin Zhang , Dalei Song , Jiangli Cao , Wenshan Yu , Wenchuan Zang","doi":"10.1016/j.apm.2025.116392","DOIUrl":null,"url":null,"abstract":"<div><div>Unmanned underwater vehicles deployed in formations for eddy measurement enhance spatial resolution and temporal continuity. However, coordinating unmanned underwater vehicle swarms presents significant challenges in accurately solving the optimization problem of maintaining high-coverage observation formations, subject to time-varying constraints imposed by dynamic eddy migration and turbulent environmental disturbances. This work proposes a hybrid framework for unmanned underwater vehicle swarm tracking of dynamic ocean eddies. The Lyapunov Guidance Vector Field generates stable guidance commands via rotational vector fields to maintain an equilateral formation, while Policy Optimization with Collaborative Adaptation optimizes real-time corrections for vortex migration and turbulence. A bidirectional collaborative mechanism facilitates parameter adaptation between modules, while Lyapunov-based constraints bound correction ranges to suppress high-frequency oscillations. Simulations and physical experiments demonstrate the effectiveness of the proposed method, achieving a spatial-temporal uniformity improvement of approximately 32.5% in static tracking scenarios and 36.5% in dynamic tracking scenarios compared to traditional methods. This work enhances unmanned underwater vehicle navigation control, improving eddy observation quality. Incorporating artificial intelligence increases automation in swarm planning, providing an effective solution for ocean eddy observation and improving oceanographic observation accuracy.</div></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":"150 ","pages":"Article 116392"},"PeriodicalIF":4.4000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient tracking of ocean eddies using unmanned underwater vehicles\",\"authors\":\"Hanbin Zhang , Dalei Song , Jiangli Cao , Wenshan Yu , Wenchuan Zang\",\"doi\":\"10.1016/j.apm.2025.116392\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Unmanned underwater vehicles deployed in formations for eddy measurement enhance spatial resolution and temporal continuity. However, coordinating unmanned underwater vehicle swarms presents significant challenges in accurately solving the optimization problem of maintaining high-coverage observation formations, subject to time-varying constraints imposed by dynamic eddy migration and turbulent environmental disturbances. This work proposes a hybrid framework for unmanned underwater vehicle swarm tracking of dynamic ocean eddies. The Lyapunov Guidance Vector Field generates stable guidance commands via rotational vector fields to maintain an equilateral formation, while Policy Optimization with Collaborative Adaptation optimizes real-time corrections for vortex migration and turbulence. A bidirectional collaborative mechanism facilitates parameter adaptation between modules, while Lyapunov-based constraints bound correction ranges to suppress high-frequency oscillations. Simulations and physical experiments demonstrate the effectiveness of the proposed method, achieving a spatial-temporal uniformity improvement of approximately 32.5% in static tracking scenarios and 36.5% in dynamic tracking scenarios compared to traditional methods. This work enhances unmanned underwater vehicle navigation control, improving eddy observation quality. Incorporating artificial intelligence increases automation in swarm planning, providing an effective solution for ocean eddy observation and improving oceanographic observation accuracy.</div></div>\",\"PeriodicalId\":50980,\"journal\":{\"name\":\"Applied Mathematical Modelling\",\"volume\":\"150 \",\"pages\":\"Article 116392\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Mathematical Modelling\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0307904X25004664\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Mathematical Modelling","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0307904X25004664","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Efficient tracking of ocean eddies using unmanned underwater vehicles
Unmanned underwater vehicles deployed in formations for eddy measurement enhance spatial resolution and temporal continuity. However, coordinating unmanned underwater vehicle swarms presents significant challenges in accurately solving the optimization problem of maintaining high-coverage observation formations, subject to time-varying constraints imposed by dynamic eddy migration and turbulent environmental disturbances. This work proposes a hybrid framework for unmanned underwater vehicle swarm tracking of dynamic ocean eddies. The Lyapunov Guidance Vector Field generates stable guidance commands via rotational vector fields to maintain an equilateral formation, while Policy Optimization with Collaborative Adaptation optimizes real-time corrections for vortex migration and turbulence. A bidirectional collaborative mechanism facilitates parameter adaptation between modules, while Lyapunov-based constraints bound correction ranges to suppress high-frequency oscillations. Simulations and physical experiments demonstrate the effectiveness of the proposed method, achieving a spatial-temporal uniformity improvement of approximately 32.5% in static tracking scenarios and 36.5% in dynamic tracking scenarios compared to traditional methods. This work enhances unmanned underwater vehicle navigation control, improving eddy observation quality. Incorporating artificial intelligence increases automation in swarm planning, providing an effective solution for ocean eddy observation and improving oceanographic observation accuracy.
期刊介绍:
Applied Mathematical Modelling focuses on research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems. A significant emerging area of research activity involves multiphysics processes, and contributions in this area are particularly encouraged.
This influential publication covers a wide spectrum of subjects including heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimization; finite volume, finite element, and boundary element procedures; modelling of inventory, industrial, manufacturing and logistics systems for viable decision making; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering.
Applied Mathematical Modelling is primarily interested in papers developing increased insights into real-world problems through novel mathematical modelling, novel applications or a combination of these. Papers employing existing numerical techniques must demonstrate sufficient novelty in the solution of practical problems. Papers on fuzzy logic in decision-making or purely financial mathematics are normally not considered. Research on fractional differential equations, bifurcation, and numerical methods needs to include practical examples. Population dynamics must solve realistic scenarios. Papers in the area of logistics and business modelling should demonstrate meaningful managerial insight. Submissions with no real-world application will not be considered.