Meng Jin;Shun Yao;Kexin Li;Xiaohua Tian;Xinbing Wang;Chenghu Zhou;Xinde Cao
{"title":"Fine-Grained UHF RFID Localization for Robotics","authors":"Meng Jin;Shun Yao;Kexin Li;Xiaohua Tian;Xinbing Wang;Chenghu Zhou;Xinde Cao","doi":"10.1109/TNET.2024.3457696","DOIUrl":null,"url":null,"abstract":"We in this paper present TiSee, an RFID-based sensing system that supports miniature robots to perform agile tasks in everyday environments. TiSee’s unique capability is that it uses a single arbitrarily-deployed antenna to locate a target with sub-cm-level accuracy and identify its orientation to within few degrees. Compared with existing solutions which rely on either antenna arrays or multiple RFID readers, TiSee is cheap, compact, and applicable to miniature robots. The idea of TiSee is to stick an RFID tag on the robot (or its gripper) and use it as a moving “antenna” to locate the tags on the target. The core of this design is a novel technique which can build a “channel” between two commercial RFID tags. Such an inter-tag channel is proved to be highly sensitive to the change in inter-tag distance and is resistant to multipath. By leveraging this channel and the mobility of the robot, we emulate an antenna array and use it for fine-grained localization and orientation estimation. Our experiments show that TiSee achieves a median accuracy of 9.5mm and 3.1° in 3D localization and orientation estimation. TiSee brings an eye-in-hand “camera” to miniature robots, supporting them to perform agile tasks in dark, cluttered, and occluded settings.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5247-5262"},"PeriodicalIF":3.0000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/ACM Transactions on Networking","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10691945/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
We in this paper present TiSee, an RFID-based sensing system that supports miniature robots to perform agile tasks in everyday environments. TiSee’s unique capability is that it uses a single arbitrarily-deployed antenna to locate a target with sub-cm-level accuracy and identify its orientation to within few degrees. Compared with existing solutions which rely on either antenna arrays or multiple RFID readers, TiSee is cheap, compact, and applicable to miniature robots. The idea of TiSee is to stick an RFID tag on the robot (or its gripper) and use it as a moving “antenna” to locate the tags on the target. The core of this design is a novel technique which can build a “channel” between two commercial RFID tags. Such an inter-tag channel is proved to be highly sensitive to the change in inter-tag distance and is resistant to multipath. By leveraging this channel and the mobility of the robot, we emulate an antenna array and use it for fine-grained localization and orientation estimation. Our experiments show that TiSee achieves a median accuracy of 9.5mm and 3.1° in 3D localization and orientation estimation. TiSee brings an eye-in-hand “camera” to miniature robots, supporting them to perform agile tasks in dark, cluttered, and occluded settings.
期刊介绍:
The IEEE/ACM Transactions on Networking’s high-level objective is to publish high-quality, original research results derived from theoretical or experimental exploration of the area of communication/computer networking, covering all sorts of information transport networks over all sorts of physical layer technologies, both wireline (all kinds of guided media: e.g., copper, optical) and wireless (e.g., radio-frequency, acoustic (e.g., underwater), infra-red), or hybrids of these. The journal welcomes applied contributions reporting on novel experiences and experiments with actual systems.