{"title":"基于卡尔曼滤波的全向机电系统传感器融合","authors":"B. Korotaj, B. Novoselnik, M. Baotic","doi":"10.1109/EDPE53134.2021.9604096","DOIUrl":null,"url":null,"abstract":"The paper describes the sensor fusion for the newly developed omnidirectional mechatronic system. To that end, the kinematic model of the platform and the chosen configuration of omnidirectional Mecanum wheels is described, as well as the principle of operation of all system sensors. The expressions are given for a discrete linear Kalman filter that fuses measurements of a magnetometer and gyroscope, and for a discrete extended Kalman filter that estimates position and orientation of the platform using additional accelerometer measurements. To be able to express the measurement equation four additional states are added to the system model. The developed sensor fusion algorithm was implemented in MATLAB/Simulink programming environment, and very accurate simulation results are reported for estimation of position and orientation of the system. Finally, the real time experimental results are reported for a prototype of the omnidirectional mobile mechatronic system.","PeriodicalId":117091,"journal":{"name":"2021 International Conference on Electrical Drives & Power Electronics (EDPE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Kalman Filter Based Sensor Fusion for Omnidirectional Mechatronic System\",\"authors\":\"B. Korotaj, B. Novoselnik, M. Baotic\",\"doi\":\"10.1109/EDPE53134.2021.9604096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes the sensor fusion for the newly developed omnidirectional mechatronic system. To that end, the kinematic model of the platform and the chosen configuration of omnidirectional Mecanum wheels is described, as well as the principle of operation of all system sensors. The expressions are given for a discrete linear Kalman filter that fuses measurements of a magnetometer and gyroscope, and for a discrete extended Kalman filter that estimates position and orientation of the platform using additional accelerometer measurements. To be able to express the measurement equation four additional states are added to the system model. The developed sensor fusion algorithm was implemented in MATLAB/Simulink programming environment, and very accurate simulation results are reported for estimation of position and orientation of the system. Finally, the real time experimental results are reported for a prototype of the omnidirectional mobile mechatronic system.\",\"PeriodicalId\":117091,\"journal\":{\"name\":\"2021 International Conference on Electrical Drives & Power Electronics (EDPE)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Electrical Drives & Power Electronics (EDPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EDPE53134.2021.9604096\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Electrical Drives & Power Electronics (EDPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDPE53134.2021.9604096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Kalman Filter Based Sensor Fusion for Omnidirectional Mechatronic System
The paper describes the sensor fusion for the newly developed omnidirectional mechatronic system. To that end, the kinematic model of the platform and the chosen configuration of omnidirectional Mecanum wheels is described, as well as the principle of operation of all system sensors. The expressions are given for a discrete linear Kalman filter that fuses measurements of a magnetometer and gyroscope, and for a discrete extended Kalman filter that estimates position and orientation of the platform using additional accelerometer measurements. To be able to express the measurement equation four additional states are added to the system model. The developed sensor fusion algorithm was implemented in MATLAB/Simulink programming environment, and very accurate simulation results are reported for estimation of position and orientation of the system. Finally, the real time experimental results are reported for a prototype of the omnidirectional mobile mechatronic system.