Show simple item record

dc.contributor.authorJaved, Mohammad Azam 19:59:18 (GMT) 19:59:18 (GMT)
dc.description.abstractThis thesis presents a novel state estimation structure, a hybrid extended Kalman filter/Kalman filter developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle (UGV) custom fitted with an inertial measurement unit, wheel encoders and a GPS. In order to enable the ARGO for autonomous applications, the proposed hybrid EKF/KF state estimator strategy is combined with the vehicle’s sensor measurements to estimate key parameters for the vehicle. Field experiments in this thesis reveal that the proposed estimation structure is able to estimate the position, velocity, orientation, and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well-suited for online applications and can incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO, and the slip results align well with the motion estimate findings.en
dc.publisherUniversity of Waterlooen
dc.subjectState Estimationen
dc.subjectMobile Roboticsen
dc.titleA State Estimation Approach for a Skid-Steered Off-Road Mobile Roboten
dc.typeMaster Thesisen
dc.subject.programMechanical Engineeringen and Mechatronics Engineeringen
uws-etd.degreeMaster of Applied Scienceen

Files in this item


This item appears in the following Collection(s)

Show simple item record


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages