{"title":"从扩展环境感知到远程水下机器人实时动态建模","authors":"Lei Lei;Yu Zhou;Jianxing Zhang","doi":"10.1109/TRO.2025.3567531","DOIUrl":null,"url":null,"abstract":"Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This article presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"3423-3441"},"PeriodicalIF":9.4000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From Extended Environment Perception Toward Real-Time Dynamic Modeling for Long-Range Underwater Robot\",\"authors\":\"Lei Lei;Yu Zhou;Jianxing Zhang\",\"doi\":\"10.1109/TRO.2025.3567531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This article presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.\",\"PeriodicalId\":50388,\"journal\":{\"name\":\"IEEE Transactions on Robotics\",\"volume\":\"41 \",\"pages\":\"3423-3441\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2025-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10989531/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Robotics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10989531/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
From Extended Environment Perception Toward Real-Time Dynamic Modeling for Long-Range Underwater Robot
Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This article presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.
期刊介绍:
The IEEE Transactions on Robotics (T-RO) is dedicated to publishing fundamental papers covering all facets of robotics, drawing on interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, and beyond. From industrial applications to service and personal assistants, surgical operations to space, underwater, and remote exploration, robots and intelligent machines play pivotal roles across various domains, including entertainment, safety, search and rescue, military applications, agriculture, and intelligent vehicles.
Special emphasis is placed on intelligent machines and systems designed for unstructured environments, where a significant portion of the environment remains unknown and beyond direct sensing or control.