{"title":"一种改进的移动机器人定位测量变量估计模型","authors":"Junsuo Qu, L. Hou, Ruijun Zhang, Zhiwei Zhang, Qipeng Zhang, Kaiming Ting","doi":"10.1075/IS.18014.QU","DOIUrl":null,"url":null,"abstract":"\n The localization and navigation technology are the key factors in the research of mobile robots. With the demand\n of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly\n prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the\n accuracy of positioning results.\n This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the\n Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the\n interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other\n factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research\n including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data\n interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved\n measurement model applied to UKF.","PeriodicalId":46494,"journal":{"name":"Interaction Studies","volume":" ","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An improved measurement variable estimation model for positioning mobile robot\",\"authors\":\"Junsuo Qu, L. Hou, Ruijun Zhang, Zhiwei Zhang, Qipeng Zhang, Kaiming Ting\",\"doi\":\"10.1075/IS.18014.QU\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The localization and navigation technology are the key factors in the research of mobile robots. With the demand\\n of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly\\n prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the\\n accuracy of positioning results.\\n This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the\\n Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the\\n interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other\\n factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research\\n including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data\\n interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved\\n measurement model applied to UKF.\",\"PeriodicalId\":46494,\"journal\":{\"name\":\"Interaction Studies\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2019-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Interaction Studies\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1075/IS.18014.QU\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMMUNICATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interaction Studies","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1075/IS.18014.QU","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMMUNICATION","Score":null,"Total":0}
An improved measurement variable estimation model for positioning mobile robot
The localization and navigation technology are the key factors in the research of mobile robots. With the demand
of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly
prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the
accuracy of positioning results.
This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the
Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the
interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other
factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research
including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data
interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved
measurement model applied to UKF.
期刊介绍:
This international peer-reviewed journal aims to advance knowledge in the growing and strongly interdisciplinary area of Interaction Studies in biological and artificial systems. Understanding social behaviour and communication in biological and artificial systems requires knowledge of evolutionary, developmental and neurobiological aspects of social behaviour and communication; the embodied nature of interactions; origins and characteristics of social and narrative intelligence; perception, action and communication in the context of dynamic and social environments; social learning.