{"title":"Set-Based State Estimation of Mobile Robots from Coarse Range Measurements","authors":"Tony X. Lin, S. Coogan, D. Sofge, Fumin Zhang","doi":"10.1109/CCTA41146.2020.9206321","DOIUrl":null,"url":null,"abstract":"This paper proposes a localization algorithm for an autonomous mobile robot equipped with binary proximity sensors that only indicate when the robot is within a fixed distance from beacons installed at known positions. Our algorithm leverages an ellipsoidal Set Membership State Estimator (SMSE) that maintains an ellipsoidal bound of the position and velocity states of the robot. The estimate incorporates knowledge of the robot's dynamics, bounds on environmental disturbances, and the binary sensor readings. The localization algorithm is motivated by an underwater scenario where accurate range or bearing measurements are often missing. We demonstrate our approach on an experimental platform using an autonomous blimp.","PeriodicalId":241335,"journal":{"name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Conference on Control Technology and Applications (CCTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCTA41146.2020.9206321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes a localization algorithm for an autonomous mobile robot equipped with binary proximity sensors that only indicate when the robot is within a fixed distance from beacons installed at known positions. Our algorithm leverages an ellipsoidal Set Membership State Estimator (SMSE) that maintains an ellipsoidal bound of the position and velocity states of the robot. The estimate incorporates knowledge of the robot's dynamics, bounds on environmental disturbances, and the binary sensor readings. The localization algorithm is motivated by an underwater scenario where accurate range or bearing measurements are often missing. We demonstrate our approach on an experimental platform using an autonomous blimp.